From 81ee434e1f25f892cde44aca30cd93e7dab3ac53 Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Mon, 19 Aug 2024 19:08:35 -0400
Subject: [PATCH 1/6] local image hosting

---
 fern/assets/images/64d7b4f-Group_138975.png   | Bin 0 -> 272408 bytes
 fern/assets/images/7fca92c-Group_138977_2.png | Bin 0 -> 3526 bytes
 fern/assets/images/8da77c9-Frame_138972.png   | Bin 0 -> 4984 bytes
 fern/assets/images/8ff146a-Group_138977.png   | Bin 0 -> 3592 bytes
 fern/assets/images/9d0694c-Symbol.svg         |   1 +
 fern/assets/images/d51d176-Group_138977_1.png | Bin 0 -> 4082 bytes
 .../command-line-interface/admin-commands.mdx |   2 +-
 .../command-reference.mdx                     |   2 +-
 fern/pages/cookbooks.mdx                      | 114 +++++++++---------
 fern/pages/cookbooks/agent-api-calls.mdx      |   2 +-
 .../cookbooks/agent-short-term-memory.mdx     |   2 +-
 .../cookbooks/agentic-multi-stage-rag.mdx     |   2 +-
 .../cookbooks/agentic-rag-mixed-data.mdx      |   2 +-
 .../cookbooks/analysis-of-financial-forms.mdx |   2 +-
 fern/pages/cookbooks/chunking-strategies.mdx  |   2 +-
 fern/pages/cookbooks/csv-agent-native-api.mdx |   2 +-
 fern/pages/cookbooks/csv-agent.mdx            |   2 +-
 .../document-parsing-for-enterprises.mdx      |   2 +-
 .../long-form-general-strategies.mdx          |   2 +-
 fern/pages/cookbooks/pdf-extractor.mdx        |   2 +-
 .../cookbooks/rag-evaluation-deep-dive.mdx    |   2 +-
 fern/pages/cookbooks/sql-agent.mdx            |   2 +-
 fern/pages/index.mdx                          |  18 +--
 23 files changed, 82 insertions(+), 81 deletions(-)
 create mode 100644 fern/assets/images/64d7b4f-Group_138975.png
 create mode 100644 fern/assets/images/7fca92c-Group_138977_2.png
 create mode 100644 fern/assets/images/8da77c9-Frame_138972.png
 create mode 100644 fern/assets/images/8ff146a-Group_138977.png
 create mode 100644 fern/assets/images/9d0694c-Symbol.svg
 create mode 100644 fern/assets/images/d51d176-Group_138977_1.png

diff --git a/fern/assets/images/64d7b4f-Group_138975.png b/fern/assets/images/64d7b4f-Group_138975.png
new file mode 100644
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diff --git a/fern/pages/command-line-interface/admin-commands.mdx b/fern/pages/command-line-interface/admin-commands.mdx
index 849bbbc2..e7f59a24 100644
--- a/fern/pages/command-line-interface/admin-commands.mdx
+++ b/fern/pages/command-line-interface/admin-commands.mdx
@@ -9,5 +9,5 @@ hidden: true
 createdAt: "Tue Oct 25 2022 17:15:13 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Mon Jul 31 2023 20:05:46 GMT+0000 (Coordinated Universal Time)"
 type: "link"
-link_url: "https://cohere-ai.readme.io/reference/finetune"
+link_url: "/reference/finetune"
 ---
diff --git a/fern/pages/command-line-interface/command-reference.mdx b/fern/pages/command-line-interface/command-reference.mdx
index aff385c2..88cab325 100644
--- a/fern/pages/command-line-interface/command-reference.mdx
+++ b/fern/pages/command-line-interface/command-reference.mdx
@@ -9,7 +9,7 @@ hidden: true
 createdAt: "Fri Sep 09 2022 15:58:35 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Mon Jul 31 2023 20:05:28 GMT+0000 (Coordinated Universal Time)"
 type: "link"
-link_url: "https://cohere-ai.readme.io/reference/admin"
+link_url: "/reference/admin"
 ---
 # Get Started with the CLI
 
diff --git a/fern/pages/cookbooks.mdx b/fern/pages/cookbooks.mdx
index b00fb5e9..2a1923ab 100644
--- a/fern/pages/cookbooks.mdx
+++ b/fern/pages/cookbooks.mdx
@@ -7,37 +7,37 @@ import { SecondaryTitleContainer } from "../components/secondary-title-container
 
 export const marco = {
   name: "Marco Del Tredici",
-  imageSrc: "https://files.readme.io/f103c96-Marco.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/f103c96-Marco.jpg",
 };
 
 export const shaan = {
   name: "Shaan Desai",
-  imageSrc: "https://files.readme.io/d17fc44-Shaan.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d17fc44-Shaan.jpg",
 };
 
 export const jason = {
   name: "Jason Jung",
-  imageSrc: "https://files.readme.io/0803e3d-Jason_Jung.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/0803e3d-Jason_Jung.jpg",
 };
 
 export const ania = {
   name: "Ania Bialas",
-  imageSrc: "https://files.readme.io/c5dc5a3-Ania.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/c5dc5a3-Ania.jpg",
 };
 
 export const aal = {
   name: "Aal Patankar",
-  imageSrc: "https://files.readme.io/d48e622-Aal.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d48e622-Aal.jpg",
 };
 
 export const alex = {
   name: "Alex Barbet",
-  imageSrc: "https://files.readme.io/bf2c763-Alex.jpg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bf2c763-Alex.jpg",
 };
 
 export const giannis = {
   name: "Giannis Chatziveroglou",
-  imageSrc: "https://files.readme.io/73153cb-giannis.jpeg",
+  imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/73153cb-giannis.jpeg",
 };
 
 export const agentCards = [
@@ -45,7 +45,7 @@ export const agentCards = [
     title: "Calendar Agent with Native Multi Step Tool",
     description:
       "A minimal working example of how to use our chat API to call tools.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["agents"],
     href: "/page/calendar-agent",
   },
@@ -53,7 +53,7 @@ export const agentCards = [
     title: "Basic Tool Use",
     description:
       "Connect large language models to external tools, like search engines, APIs, and databases, to access and utilise a wider range of data.",
-    imageSrc: "https://files.readme.io/bad278b-Community_Demo_2.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
     tags: ["agents"],
     href: "/page/basic-tool-use",
   },
@@ -61,7 +61,7 @@ export const agentCards = [
     title: "Multi-Step Tool Use",
     description:
       "Multi-step tool use allows developers to connect Cohere's models to external tools like search engines, APIs, and databases.",
-    imageSrc: "https://files.readme.io/8b24122-Community_Demo_3.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
     tags: ["agents", "oss"],
     href: "/page/basic-multi-step",
   },
@@ -69,7 +69,7 @@ export const agentCards = [
     title: "A Data Analyst Agent Built with Cohere and Langchain",
     description:
       "Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: ["agents", "oss"],
     href: "/page/data-analyst-agent",
   },
@@ -77,7 +77,7 @@ export const agentCards = [
     title: "Short-Term Memory Handling for Agents",
     description:
       "A walkthrough of how to use Langchain cohere_react_agent to effectively manage short-term chat history that contains tool calls with Langchain.",
-    imageSrc: "https://files.readme.io/d86be24-Community_Demo_6.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
     tags: ["agents"],
     href: "/page/agent-short-term-memory",
     authors: [marco],
@@ -86,7 +86,7 @@ export const agentCards = [
     title: "Agent API Calls",
     description:
       "A walkthrough of how to use Langchain cohere_react_agent to make API calls to external services that require regex.",
-    imageSrc: "https://files.readme.io/083d9e2-Community_Demo_7.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
     tags: ["agents"],
     href: "/page/agent-api-calls",
     authors: [marco],
@@ -95,7 +95,7 @@ export const agentCards = [
     title: "Financial CSV Agent with Langchain",
     description:
       "The notebook demonstrates how to setup a Langchain Cohere ReAct Agent to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
-    imageSrc: "https://files.readme.io/13cb578-Community_Demo_8.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
     tags: ["agents", "oss"],
     href: "/page/csv-agent",
     authors: [shaan],
@@ -104,7 +104,7 @@ export const agentCards = [
     title: "Agentic RAG for PDFs with mixed data",
     description:
       "A walkthrough of how to use Langchain cohere_react_agent to run RAG as an agent tool to handle PDFs with mixed table and text data.",
-    imageSrc: "https://files.readme.io/142e385-Community_Demo_9.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
     tags: ["agents"],
     href: "/page/agentic-rag-mixed-data",
     authors: [shaan],
@@ -113,7 +113,7 @@ export const agentCards = [
     title: "SQL Agent",
     description:
       "In this notebook we explore how to setup a Cohere ReAct Agent to answer questions over SQL Databases using Langchain's SQLDBToolkit.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["agents"],
     href: "/page/sql-agent",
     authors: [shaan],
@@ -122,7 +122,7 @@ export const agentCards = [
     title: "Financial CSV Agent with Native Multi-Step Cohere API",
     description:
       "This notebook demonstrates how to setup a Cohere Native API sequence of tool calls to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
-    imageSrc: "https://files.readme.io/bad278b-Community_Demo_2.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
     tags: ["agents"],
     href: "/page/csv-agent-native-api",
     authors: [jason],
@@ -130,7 +130,7 @@ export const agentCards = [
   {
     title: "PDF Extractor with Native Multi Step Tool Use",
     description: "How we can leverage agents to extract information from PDFs?",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["agents"],
     href: "/page/pdf-extractor",
     authors: [jason],
@@ -138,7 +138,7 @@ export const agentCards = [
   {
     title: "Agentic Multi-Stage RAG with Cohere Tools API",
     description: "How to use Agents to improve RAG performance.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: ["agents"],
     href: "/page/agentic-multi-stage-rag",
     authors: [jason],
@@ -150,7 +150,7 @@ export const ossCards = [
     title: "Multi-Step Tool Use",
     description:
       "Multi-step tool use allows developers to connect Cohere's models to external tools like search engines, APIs, and databases.",
-    imageSrc: "https://files.readme.io/8b24122-Community_Demo_3.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
     tags: ["agents", "oss"],
     href: "/page/basic-multi-step",
   },
@@ -158,7 +158,7 @@ export const ossCards = [
     title: "A Data Analyst Agent Built with Cohere and Langchain",
     description:
       "Build a data analyst agent with Python and Cohere's Command R+ mode and Langchain.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: ["agents", "oss"],
     href: "/page/data-analyst-agent",
   },
@@ -166,7 +166,7 @@ export const ossCards = [
     title: "Financial CSV Agent with Langchain",
     description:
       "The notebook demonstrates how to setup a Langchain Cohere ReAct Agent to answer questions over the income statement and balance sheet from Apple's SEC10K 2020 form.",
-    imageSrc: "https://files.readme.io/13cb578-Community_Demo_8.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
     tags: ["agents", "oss"],
     href: "/page/csv-agent",
     authors: [shaan],
@@ -174,7 +174,7 @@ export const ossCards = [
   {
     title: "Multilingual Search with Cohere and Langchain",
     description: "Multilingual search with Cohere and Langchain.",
-    imageSrc: "https://files.readme.io/d86be24-Community_Demo_6.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
     tags: ["search", "oss"],
     href: "/page/multilingual-search",
   },
@@ -182,7 +182,7 @@ export const ossCards = [
     title: "Creating a QA Bot From Technical Documentation",
     description:
       "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["oss", "search", "rag"],
     href: "/page/creating-a-qa-bot",
   },
@@ -193,7 +193,7 @@ export const searchCards = [
     title: "Wikipedia Semantic Search with Cohere Embedding Archives",
     description:
       "Find relevant Wikipedia passages with semantic search and Cohere embeddings.",
-    imageSrc: "https://files.readme.io/083d9e2-Community_Demo_7.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
     tags: ["search"],
     href: "/page/wikipedia-semantic-search",
   },
@@ -202,7 +202,7 @@ export const searchCards = [
       "Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution",
     description:
       "Learn how to use Cohere's Embed Jobs and Pinecone's serverless solution to perform semantic search.",
-    imageSrc: "https://files.readme.io/13cb578-Community_Demo_8.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
     tags: ["cloud", "search"],
     href: "/page/embed-jobs-serverless-pinecone",
   },
@@ -210,7 +210,7 @@ export const searchCards = [
     title: "End-to-end RAG using Elasticsearch and Cohere",
     description:
       "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["search", "rag", "cloud"],
     href: "/page/elasticsearch-and-cohere",
   },
@@ -218,7 +218,7 @@ export const searchCards = [
     title: "Semantic Search with Cohere Embed Jobs",
     description:
       "Learn how to use Cohere Embed Jobs to create semantic search functionality.",
-    imageSrc: "https://files.readme.io/bad278b-Community_Demo_2.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
     tags: ["search"],
     href: "/page/embed-jobs",
   },
@@ -226,14 +226,14 @@ export const searchCards = [
     title: "Basic Semantic Search",
     description:
       "Learn how to build a simple semantic search engine using sentence embeddings.",
-    imageSrc: "https://files.readme.io/bad278b-Community_Demo_2.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
     tags: ["search"],
     href: "/page/basic-semantic-search",
   },
   {
     title: "Multilingual Search with Cohere and Langchain",
     description: "Multilingual search with Cohere and Langchain.",
-    imageSrc: "https://files.readme.io/d86be24-Community_Demo_6.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
     tags: ["search", "oss"],
     href: "/page/multilingual-search",
   },
@@ -241,7 +241,7 @@ export const searchCards = [
     title: "Wikipedia Semantic Search with Cohere + Weaviate",
     description:
       "Search 10 million Wikipedia vectors with Cohere's multilingual model and Weaviate's public dataset.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["search"],
     href: "/page/wikipedia-search-with-weaviate",
   },
@@ -249,7 +249,7 @@ export const searchCards = [
     title: "Creating a QA Bot From Technical Documentation",
     description:
       "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["oss", "search", "rag"],
     href: "/page/creating-a-qa-bot",
   },
@@ -257,7 +257,7 @@ export const searchCards = [
     title: "Demo of Rerank",
     description:
       "Improve search results with Cohere's Relevance Endpoint, which reranks documents for better accuracy.",
-    imageSrc: "https://files.readme.io/d86be24-Community_Demo_6.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d86be24-Community_Demo_6.png",
     tags: ["search"],
     href: "/page/rerank-demo",
   },
@@ -269,7 +269,7 @@ export const cloudCards = [
       "Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution",
     description:
       "Learn how to use Cohere's Embed Jobs and Pinecone's serverless solution to perform semantic search.",
-    imageSrc: "https://files.readme.io/13cb578-Community_Demo_8.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/13cb578-Community_Demo_8.png",
     tags: ["cloud", "search"],
     href: "/page/embed-jobs-serverless-pinecone",
   },
@@ -277,7 +277,7 @@ export const cloudCards = [
     title: "End-to-end RAG using Elasticsearch and Cohere",
     description:
       "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["search", "rag", "cloud"],
     href: "/page/elasticsearch-and-cohere",
   },
@@ -288,7 +288,7 @@ export const ragCards = [
     title: "Basic RAG",
     description:
       "RAG boosts the accuracy of language models by combining them with a retrieval system.",
-    imageSrc: "https://files.readme.io/142e385-Community_Demo_9.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
     tags: ["rag"],
     href: "/page/basic-rag",
   },
@@ -296,14 +296,14 @@ export const ragCards = [
     title: "End-to-end RAG using Elasticsearch and Cohere",
     description:
       "Learn how to use Cohere and Elastic for semantic search and retrieval-augmented generation.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["search", "rag", "cloud"],
     href: "/page/elasticsearch-and-cohere",
   },
   {
     title: "Chunking Strategies",
     description: "Explore chunking strategies for RAG systems.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["rag"],
     href: "/page/chunking-strategies",
     authors: [ania],
@@ -312,7 +312,7 @@ export const ragCards = [
     title: "Migrating Monolithic Prompts to Command-R with RAG",
     description:
       "Command-R simplifies prompt migration to RAG, reducing hallucination and improving conciseness and grounding.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: ["rag"],
     href: "/page/migrating-prompts",
   },
@@ -320,7 +320,7 @@ export const ragCards = [
     title: "RAG With Chat Embed and Rerank via Pinecone",
     description:
       "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint.",
-    imageSrc: "https://files.readme.io/8b24122-Community_Demo_3.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
     tags: ["rag"],
     href: "/page/rag-with-chat-embed",
   },
@@ -328,14 +328,14 @@ export const ragCards = [
     title: "Creating a QA Bot From Technical Documentation",
     description:
       "Create a chatbot that answers user questions based on technical documentation using Cohere embeddings and LlamaIndex.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["oss", "search", "rag"],
     href: "/page/creating-a-qa-bot",
   },
   {
     title: "Deep Dive Into RAG Evaluation",
     description: "Learn how to evaluate RAG models.",
-    imageSrc: "https://files.readme.io/8b24122-Community_Demo_3.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
     tags: ["rag"],
     href: "/page/rag-evaluation-deep-dive",
     authors: [marco, aal],
@@ -347,7 +347,7 @@ export const summarizationCards = [
     title: "Analysis of Form 10-K/10-Q Using Cohere and RAG",
     description:
       "Jumpstart financial analysis of 10-Ks or 10-Qs with Cohere's Command model and LlamaIndex tooling.",
-    imageSrc: "https://files.readme.io/083d9e2-Community_Demo_7.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
     tags: ["summarization"],
     href: "/page/analysis-of-financial-forms",
     authors: [alex],
@@ -356,7 +356,7 @@ export const summarizationCards = [
     title: "Long Form General Strategies",
     description:
       "Techniques to address lengthy documents exceeding the context window of LLMs.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: ["summarization"],
     href: "/page/long-form-general-strategies",
     authors: [ania],
@@ -365,7 +365,7 @@ export const summarizationCards = [
     title: "Summarization Evals",
     description:
       "This cookbook demonstrates an approach to evaluating summarization tasks using LLM evaluation.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: ["summarization"],
     href: "/page/summarization-evals",
   },
@@ -373,7 +373,7 @@ export const summarizationCards = [
     title: "Grounded Summarization Using Command R",
     description:
       "Learn how to summarise long documents with citations, reducing cost and improving latency.",
-    imageSrc: "https://files.readme.io/bad278b-Community_Demo_2.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bad278b-Community_Demo_2.png",
     tags: ["summarization"],
     href: "/page/grounded-summarization",
   },
@@ -384,7 +384,7 @@ export const otherCards = [
     title: "Fueling Generative Content with Keyword Research",
     description:
       "Enhance content creation with keyword-based topic clusters, generating blog ideas with Cohere's Chat model.",
-    imageSrc: "https://files.readme.io/8b24122-Community_Demo_3.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8b24122-Community_Demo_3.png",
     tags: [],
     href: "/page/fueling-generative-content",
   },
@@ -392,7 +392,7 @@ export const otherCards = [
     title: "Text Classification Using Embeddings",
     description:
       "Build a text classifier with Cohere embeddings. This notebook shows you how to train a sentiment analysis model with a small dataset.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: [],
     href: "/page/text-classification-using-embeddings",
   },
@@ -400,21 +400,21 @@ export const otherCards = [
     title: "Article Recommender with Text Embedding Classification Extraction",
     description:
       "Improve news article recommendations with embeddings, text classification, and keyword extraction.",
-    imageSrc: "https://files.readme.io/142e385-Community_Demo_9.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/142e385-Community_Demo_9.png",
     tags: [],
     href: "/page/article-recommender-with-text-embeddings",
   },
   {
     title: "Advanced Document Parsing For Enterprises",
     description: "Learn how to parse PDFs into text with a real-world example.",
-    imageSrc: "https://files.readme.io/083d9e2-Community_Demo_7.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
     tags: [],
     href: "/page/document-parsing-for-enterprises",
     authors: [
       giannis,
       {
         name: "Justin Lee",
-        imageSrc: "https://files.readme.io/3678fac-Justin.jpg",
+        imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/3678fac-Justin.jpg",
       },
     ],
   },
@@ -422,7 +422,7 @@ export const otherCards = [
     title: "Pondr, Fostering Connection through Good Conversation",
     description:
       "Learn how to create Pondr, a game that fosters connections and meaningful conversations with friends and strangers.",
-    imageSrc: "https://files.readme.io/86191a6-Community_Demo_5.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/86191a6-Community_Demo_5.png",
     tags: [],
     href: "/page/pondr",
   },
@@ -430,21 +430,21 @@ export const otherCards = [
     title: "Hello World! Meet Language AI",
     description:
       "General quickstart with basic instructions on how to get started with generative AI.",
-    imageSrc: "https://files.readme.io/083d9e2-Community_Demo_7.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/083d9e2-Community_Demo_7.png",
     tags: [],
     href: "/page/hello-world-meet-ai",
   },
   {
     title: "Topic Modeling AI Papers",
     description: "Learn how to build a topic modeling pipeline in Python.",
-    imageSrc: "https://files.readme.io/5523913-Community_Demo_1.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/5523913-Community_Demo_1.png",
     tags: [],
     href: "/page/topic-modeling-ai-papers",
   },
   {
     title: "Analyzing Hacker News with Six Language Understanding Methods",
     description: "Learn how to analyze textual data using Cohere's tools.",
-    imageSrc: "https://files.readme.io/28ee583-Community_Demo_4.png",
+    imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/28ee583-Community_Demo_4.png",
     tags: [],
     href: "/page/analyzing-hacker-news",
   },
@@ -549,13 +549,13 @@ export const UseCasesContainer = () => (
   <div
     className="max-md:hidden absolute inset-0 -z-10 bg-cover bg-[right_center] bg-no-repeat"
     style={{
-      backgroundImage: `url(https://files.readme.io/9bd3335-bg-apps-desktop.svg)`,
+      backgroundImage: `url(https://fern-image-hosting.s3.amazonaws.com/cohere/9bd3335-bg-apps-desktop.svg)`,
     }}
   />
   <div
     className="md:hidden absolute inset-0 -z-10 bg-cover bg-right-bottom bg-no-repeat"
     style={{
-      backgroundImage: `url(https://files.readme.io/f08bf1e-landingPage.svg)`,
+      backgroundImage: `url(https://fern-image-hosting.s3.amazonaws.com/cohere/f08bf1e-landingPage.svg)`,
     }}
   />
   <div className="max-w-[1100px] mx-auto page-title-container px-4 md:px-6 lg:px-9">
diff --git a/fern/pages/cookbooks/agent-api-calls.mdx b/fern/pages/cookbooks/agent-api-calls.mdx
index e51ee61d..ceb5a683 100644
--- a/fern/pages/cookbooks/agent-api-calls.mdx
+++ b/fern/pages/cookbooks/agent-api-calls.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Marco Del Tredici",
-      imageSrc: "https://files.readme.io/f103c96-Marco.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/f103c96-Marco.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/agent-short-term-memory.mdx b/fern/pages/cookbooks/agent-short-term-memory.mdx
index 004c32f8..a3429548 100644
--- a/fern/pages/cookbooks/agent-short-term-memory.mdx
+++ b/fern/pages/cookbooks/agent-short-term-memory.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Marco Del Tredici",
-      imageSrc: "https://files.readme.io/f103c96-Marco.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/f103c96-Marco.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/agentic-multi-stage-rag.mdx b/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
index 19421f05..c1881a1b 100644
--- a/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
+++ b/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Jason Jung",
-      imageSrc: "https://files.readme.io/0803e3d-Jason_Jung.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/0803e3d-Jason_Jung.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/agentic-rag-mixed-data.mdx b/fern/pages/cookbooks/agentic-rag-mixed-data.mdx
index 7d8f3efc..0d15e415 100644
--- a/fern/pages/cookbooks/agentic-rag-mixed-data.mdx
+++ b/fern/pages/cookbooks/agentic-rag-mixed-data.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Shaan Desai",
-      imageSrc: "https://files.readme.io/d17fc44-Shaan.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d17fc44-Shaan.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/analysis-of-financial-forms.mdx b/fern/pages/cookbooks/analysis-of-financial-forms.mdx
index ca6a7a89..74a4caf1 100644
--- a/fern/pages/cookbooks/analysis-of-financial-forms.mdx
+++ b/fern/pages/cookbooks/analysis-of-financial-forms.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Alex Barbet",
-      imageSrc: "https://files.readme.io/bf2c763-Alex.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/bf2c763-Alex.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/chunking-strategies.mdx b/fern/pages/cookbooks/chunking-strategies.mdx
index 62dbfdc7..14855fd2 100644
--- a/fern/pages/cookbooks/chunking-strategies.mdx
+++ b/fern/pages/cookbooks/chunking-strategies.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Ania Bialas",
-      imageSrc: "https://files.readme.io/c5dc5a3-Ania.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/c5dc5a3-Ania.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/csv-agent-native-api.mdx b/fern/pages/cookbooks/csv-agent-native-api.mdx
index 3d6f7386..d5a9a52f 100644
--- a/fern/pages/cookbooks/csv-agent-native-api.mdx
+++ b/fern/pages/cookbooks/csv-agent-native-api.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Jason Jung",
-      imageSrc: "https://files.readme.io/0803e3d-Jason_Jung.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/0803e3d-Jason_Jung.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/csv-agent.mdx b/fern/pages/cookbooks/csv-agent.mdx
index 7af8ade8..3865a095 100644
--- a/fern/pages/cookbooks/csv-agent.mdx
+++ b/fern/pages/cookbooks/csv-agent.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Shaan Desai",
-      imageSrc: "https://files.readme.io/d17fc44-Shaan.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d17fc44-Shaan.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/document-parsing-for-enterprises.mdx b/fern/pages/cookbooks/document-parsing-for-enterprises.mdx
index b5edce8f..5490ec55 100644
--- a/fern/pages/cookbooks/document-parsing-for-enterprises.mdx
+++ b/fern/pages/cookbooks/document-parsing-for-enterprises.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Giannis Chatziveroglou",
-      imageSrc: "https://files.readme.io/73153cb-giannis.jpeg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/73153cb-giannis.jpeg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/long-form-general-strategies.mdx b/fern/pages/cookbooks/long-form-general-strategies.mdx
index 31165ae1..cb5210d1 100644
--- a/fern/pages/cookbooks/long-form-general-strategies.mdx
+++ b/fern/pages/cookbooks/long-form-general-strategies.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Ania Bialas",
-      imageSrc: "https://files.readme.io/c5dc5a3-Ania.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/c5dc5a3-Ania.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/pdf-extractor.mdx b/fern/pages/cookbooks/pdf-extractor.mdx
index 25193576..220d1c84 100644
--- a/fern/pages/cookbooks/pdf-extractor.mdx
+++ b/fern/pages/cookbooks/pdf-extractor.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Jason Jung",
-      imageSrc: "https://files.readme.io/0803e3d-Jason_Jung.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/0803e3d-Jason_Jung.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/rag-evaluation-deep-dive.mdx b/fern/pages/cookbooks/rag-evaluation-deep-dive.mdx
index 42de0df6..ce395368 100644
--- a/fern/pages/cookbooks/rag-evaluation-deep-dive.mdx
+++ b/fern/pages/cookbooks/rag-evaluation-deep-dive.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Marco Del Tredici",
-      imageSrc: "https://files.readme.io/f103c96-Marco.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/f103c96-Marco.jpg",
     },
   ]}
 />
diff --git a/fern/pages/cookbooks/sql-agent.mdx b/fern/pages/cookbooks/sql-agent.mdx
index eb230e2e..388bc662 100644
--- a/fern/pages/cookbooks/sql-agent.mdx
+++ b/fern/pages/cookbooks/sql-agent.mdx
@@ -10,7 +10,7 @@ import { CookbookHeader } from "../../components/cookbook-header";
   authors={[
     {
       name: "Shaan Desai",
-      imageSrc: "https://files.readme.io/d17fc44-Shaan.jpg",
+      imageSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d17fc44-Shaan.jpg",
     },
   ]}
 />
diff --git a/fern/pages/index.mdx b/fern/pages/index.mdx
index 4bccfbba..22be4954 100644
--- a/fern/pages/index.mdx
+++ b/fern/pages/index.mdx
@@ -90,28 +90,28 @@ export const EndpointLink = ({ href, title }) => (
 export const cards = [
   {
     href: "/docs",
-    imgSrc: "https://files.readme.io/8da77c9-Frame_138972.png",
+    imgSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8da77c9-Frame_138972.png",
     title: "Guides and concepts",
     description:
       "Understand how to use our API on a deeper level. Train and customize the model to work for you.",
   },
   {
     href: "/reference/about",
-    imgSrc: "https://files.readme.io/8ff146a-Group_138977.png",
+    imgSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/8ff146a-Group_138977.png",
     title: "API reference",
     description:
       "Integrate natural language processing and generation into your products with a few lines of code.",
   },
   {
     href: "/release-notes",
-    imgSrc: "https://files.readme.io/d51d176-Group_138977_1.png",
+    imgSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/d51d176-Group_138977_1.png",
     title: "Release notes",
     description:
       "Keep up with the latest releases and platform updates from Cohere.",
   },
   {
     href: "/page/cookbooks",
-    imgSrc: "https://files.readme.io/7fca92c-Group_138977_2.png",
+    imgSrc: "https://fern-image-hosting.s3.amazonaws.com/cohere/7fca92c-Group_138977_2.png",
     title: "Cookbooks",
     description:
       "A collection of resources to help developers get up and running with Cohere.",
@@ -167,7 +167,7 @@ export const endpoints = [
         className="group hidden rounded-lg bg-cover bg-no-repeat p-6 md:!block md:h-[190px] lg:h-[230px]"
         style={{
           backgroundImage:
-            "url('https://files.readme.io/55d9cde-InfoCards.png')",
+            "url('https://fern-image-hosting.s3.amazonaws.com/cohere/55d9cde-InfoCards.png')",
         }}
       >
         <a
@@ -178,7 +178,7 @@ export const endpoints = [
           <div className="flex h-full w-[420px] flex-col items-start justify-between">
             <div className="flex flex-row items-start">
               <img
-                src="https://files.readme.io/9d0694c-Symbol.svg"
+                src="https://fern-image-hosting.s3.amazonaws.com/cohere/9d0694c-Symbol.svg"
                 alt=""
                 className="h-7 w-7 lg:h-10 lg:w-10"
               />
@@ -191,7 +191,7 @@ export const endpoints = [
           </div>
           <div className="flex h-full md:w-1/2">
             <img
-              src="https://files.readme.io/64d7b4f-Group_138975.png"
+              src="https://fern-image-hosting.s3.amazonaws.com/cohere/64d7b4f-Group_138975.png"
               alt=""
               className="'duration-400 group-hover:pr-2' flex items-center object-contain transition-all ease-in-out group-hover:pl-1"
             />
@@ -203,7 +203,7 @@ export const endpoints = [
   <div
     className="block h-full w-full rounded-lg bg-cover bg-no-repeat p-6 md:hidden"
     style={{
-      backgroundImage: "url('https://files.readme.io/ddd53ed-Mask_group.png')",
+      backgroundImage: "url('https://fern-image-hosting.s3.amazonaws.com/cohere/ddd53ed-Mask_group.png')",
     }}
   >
     <a
@@ -214,7 +214,7 @@ export const endpoints = [
       <div className="flex h-full flex-col justify-between">
         <div className="flex flex-row">
           <img
-            src="https://files.readme.io/9d0694c-Symbol.svg"
+            src="https://fern-image-hosting.s3.amazonaws.com/cohere/9d0694c-Symbol.svg"
             alt=""
             className="h-7 w-7 lg:h-10 lg:w-10"
           />

From fbccd3b20e5929036b6dcb49766fe93f693dab2d Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Tue, 20 Aug 2024 12:12:40 -0400
Subject: [PATCH 2/6] hidden page

---
 fern/assets/input.css                         |  96 ++++++++++
 ...g-command-r.md => prompting-command-r.mdx} | 170 ++++--------------
 fern/v1.yml                                   |   3 +
 fern/v2.yml                                   |   3 +
 4 files changed, 133 insertions(+), 139 deletions(-)
 rename fern/pages/text-generation/prompt-engineering/{prompting-command-r.md => prompting-command-r.mdx} (78%)

diff --git a/fern/assets/input.css b/fern/assets/input.css
index f01dfe94..035c30c5 100644
--- a/fern/assets/input.css
+++ b/fern/assets/input.css
@@ -512,3 +512,99 @@ button[class^="Sidebar-link-buttonWrapper"] {
   max-width: 400px !important;
   overflow: wrap !important;
 }
+
+.orange-text {
+  color: #e69138 !important;
+}
+.dark-orange-text{
+  color: #b45f06 !important;
+}
+.dark-red-text {
+  color: #a20302 !important;
+}
+.red-text {
+  color: #cc0100 !important;
+}
+.bloody-red-text {
+  color: #a31515;
+}
+.dark-pink-text {
+  color: #b80672;
+}
+.magenta-text {
+  color: #cc669f !important;
+  white-space : pre-wrap !important;
+}
+.dark-magenta-text {
+  color: #78345a !important;
+}
+.sangria-text {
+  color: #981ea0 !important;
+}
+.dark-sangria-text {
+  color: #6b1570 !important;
+}
+.brown-text {
+  color: #5b0f00 !important;
+}
+.green-text {
+  color: #6aa84f !important;
+}
+.grass-green-text {
+  color: #38761d;
+}
+.dark-green-text {
+  color: #284e13 !important;
+}
+.quartz-text {
+  color: #a64d79 !important;
+}
+.blue-text {
+  color: #4078f2 !important;
+}
+.dark-blue-text {
+  color: #1255cc !important;
+}
+.purple-text {
+  color: #674ea7 !important;
+}
+.dark-purple-text {
+  color: #361c75 !important;
+}
+.markdown-body code {
+  color: inherit !important;
+}
+.cm-s-neo .cm-keyword {
+  color: inherit !important;  
+}
+.cm-s-neo .cm-variable {
+  color: inherit !important;  
+}
+.cm-s-neo .cm-string {
+  color: inherit !important;  
+}
+.cm-s-neo .cm-property {
+  color: inherit !important;  
+}
+.theme-light {
+  color: #cc669f !important;
+}
+.override {
+  line-height:100% !important;
+}
+.yellow-highlight{
+          background-color: yellow;
+          display: inline-block;
+          width: 100px; /* Adjust the width as needed */
+          /*height: 20px;*/ /* Adjust the height as needed */
+}
+.code-block {
+  background-color: rgb(233 230 222);
+  border-width: 1px;
+  border-style: solid;
+  border-color: rgb(228 222 210);
+  display: block !important;
+  overflow: auto;
+  padding: 1em;
+  border-radius:3px;
+}
diff --git a/fern/pages/text-generation/prompt-engineering/prompting-command-r.md b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
similarity index 78%
rename from fern/pages/text-generation/prompt-engineering/prompting-command-r.md
rename to fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
index 56972389..9e78e21f 100644
--- a/fern/pages/text-generation/prompt-engineering/prompting-command-r.md
+++ b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
@@ -11,106 +11,6 @@ createdAt: "Thu Mar 14 2024 17:14:34 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Mon May 06 2024 19:22:34 GMT+0000 (Coordinated Universal Time)"
 ---
 
-<style>
-  {`
-  .orange-text {
-  	color: #e69138 !important;
-  }
-  .dark-orange-text{
-  	color: #b45f06 !important;
-  }
-  .dark-red-text {
-  	color: #a20302 !important;
-  }
-  .red-text {
-  	color: #cc0100 !important;
-  }
-  .bloody-red-text {
-  	color: #a31515;
-  }
-  .dark-pink-text {
-  	color: #b80672;
-  }
-  .magenta-text {
-  	color: #cc669f !important;
-    white-space : pre-wrap !important;
-  }
-  .dark-magenta-text {
-  	color: #78345a !important;
-  }
-  .sangria-text {
-  	color: #981ea0 !important;
-  }
-  .dark-sangria-text {
-  	color: #6b1570 !important;
-  }
-  .brown-text {
-  	color: #5b0f00 !important;
-  }
-  .green-text {
-  	color: #6aa84f !important;
-  }
-  .grass-green-text {
-  	color: #38761d;
-  }
-  .dark-green-text {
-  	color: #284e13 !important;
-  }
-  .quartz-text {
-    color: #a64d79 !important;
-  }
-  .blue-text {
-  	color: #4078f2 !important;
-  }
-  .dark-blue-text {
-  	color: #1255cc !important;
-  }
-  .purple-text {
-  	color: #674ea7 !important;
-  }
-  .dark-purple-text {
-  	color: #361c75 !important;
-  }
-  .markdown-body code {
-    color: inherit !important;
-  }
-  .cm-s-neo .cm-keyword {
-    color: inherit !important;  
-  }
-  .cm-s-neo .cm-variable {
-    color: inherit !important;  
-  }
-  .cm-s-neo .cm-string {
-    color: inherit !important;  
-  }
-  .cm-s-neo .cm-property {
-    color: inherit !important;  
-  }
-  .theme-light {
-  	color: #cc669f !important;
-  }
-  .override {
-  	line-height:100% !important;
-  }
-  .yellow-highlight{
-            background-color: yellow;
-            display: inline-block;
-            width: 100px; /* Adjust the width as needed */
-            /*height: 20px;*/ /* Adjust the height as needed */
-  }
-  .code-block {
-    background-color: rgb(233 230 222);
-    border-width: 1px;
-    border-style: solid;
-    border-color: rgb(228 222 210);
-    display: block !important;
-    overflow: auto;
-    padding: 1em;
-    border-radius:3px;
-  }
-  `}
-</style>
-
 Getting an LLM to do what you want and perform well on your task often requires some amount of prompt engineering. Depending on the complexity of the task and the strength of the model, this can be time consuming. Similarly, if you are trying to compare two models in a fair way, it is hard to know what differences in performance are due to actual superiority of a model vs an unoptimized prompt. At minimum, it is important to do simple things like making sure you are using the correct special tokens which can change from one family of model to the next but can have an important impact on performance. These tokens do things like indicate the beginning and end of prompts and distinguish between user and chatbot utterances.
 
 The easiest way to make sure your prompts will work well with Command R is to use our [tokenizer on Hugging Face](https://huggingface.co/CohereForAI/c4ai-command-r-v01) if your use-case is covered by the baked-in defaults. In this doc we will go over the structure of our prompts and general best practices on how to tweak it in a way that will have it performing best on your tasks. This gives you the control over how the model behaves to tweak and experiment what fits your unique use case the best.
@@ -144,32 +44,26 @@ Note that you could get the same result if you were using the HuggingFace Tokeni
 >
 > <span class="dark-green-text"># System Preamble</span>
 >
-> <div class="code-block">
-> <span class="green-text">## Basic Rules</span>
-> <span class="green-text">You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. </span></div>
+> <div class="code-block"><span class="green-text">## Basic Rules</span><span class="green-text">You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. </span></div>
 >
 > <br />
 >
 > <span class="dark-purple-text"># User Preamble</span>
 >
-> <div class="code-block"><span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.</span></div>
+> <div class="code-block"><span class="purple-text">## Task and Context</span><span class="purple-text">You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.</span></div>
 >
 > <br />
 >
 > <span class="dark-sangria-text">## Style Guide</span>
 >
-> <div class="code-block">
-> <span class="dark-sangria-text">Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.</span></div>
+> <div class="code-block"><span class="dark-sangria-text">Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.</span></div>
 >
 > <br />
 >
 > <span class="magenta-text">## Available Tools</span>
 >
-> <div class="code-block">
 > <span class="magenta-text">Here is a list of tools that you have available to you:</span>
->
-> <pre><span class="magenta-text">```python PYTHON
+> ```python PYTHON
 > def internet_search(query: str) -> List[Dict]:
 >      """Returns a list of relevant document snippets for a textual query retrieved from the internet
 >      Args:
@@ -183,22 +77,22 @@ Note that you could get the same result if you were using the HuggingFace Tokeni
 >     """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
 >     """
 >     pass
-> ```</span></pre>
+> ```
 >
 > <span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|USER_TOKEN|></span>  
 > <span class="orange-text "> What's the biggest penguin in the world?</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span>  
-> <span class="magenta-text">Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the \`directly-answer\` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
+> <span class="magenta-text">Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the \`directly-answer\` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:</span>
 >
-> <pre><span class="magenta-text">```json JSON
+> ```json JSON
 > [
 >     {
 >         "tool_name": title of the tool in the specification,
 >         "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
 >     }
 > ]
-> ```</span></pre>
+> ```
 >
-> <span class="brown-text">\<|END_OF_TURN_TOKEN|></span>""" </div>
+> <span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 ### Structured Prompt Templates
 
@@ -206,8 +100,7 @@ The fully rendered prompt above contains a lot of information but it is actually
 
 > tool_prompt_template =
 >
-> <div class="code-block">"""<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">
-> \<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
+> """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
 > <span class="red-text">{SAFETY_PREAMBLE}</span>
 >
 > <br />
@@ -235,7 +128,7 @@ The fully rendered prompt above contains a lot of information but it is actually
 > <span class="magenta-text">## Available Tools</span>
 >
 > <span class="magenta-text">{TOOLS}</span>  
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""</div>
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 We can see that the prompt is set up in a structured way where we have sections for things like the <span class="green-text ">basic rules</span> we want the model to follow, the <span class="purple-text">task</span> we want it to solve, and the <span class="magenta-text">style</span> in which it should write its output in.
 
@@ -243,7 +136,7 @@ We will take a closer look at these sections later but first lets see how the te
 
 > augmented_gen_prompt_template =
 >
-> <div class="code-block">"""<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
+> """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
 > <span class="red-text">{SAFETY_PREAMBLE}</span>
 >
 > <span class="dark-green-text"># System Preamble</span>  
@@ -259,7 +152,7 @@ We will take a closer look at these sections later but first lets see how the te
 >
 > <span class="yellow-highlight"> </span>
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span class="brown-text "><span style="color:#a31515;background-color:#ffff00;"> {TOOL_OUTPUTS}</span>\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""</div>
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> {TOOL_OUTPUTS}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 Here we can see that the overall structure of the template is very similar between tool use and augmented generation. There are, however, two spots that differ which are highlighted. The first is that we have removed the tool definitions which come after the style guide (you can see that there's no longer an <span class="magenta-text">## Available Tools</span> section), and the second is that we’ve added the <span style="color:#a31515;background-color:#ffff00;">TOOL_OUTPUTS</span> retrieved from search after the chat history.
 
@@ -273,16 +166,15 @@ The rendered chat history is quite simple and the only thing to note is that eac
 
 > <span class="orange-text ">rendered_chat_history</span> =
 >
-> <div class="code-block">
-> """<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="brown-text "><span class="dark-orange-text ">\<|USER_TOKEN|></span><span class="orange-text "> What's the biggest penguin in the world? </span>\<|END_OF_TURN_TOKEN|></span>"""</div>
+> """<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="brown-text "><span class="dark-orange-text ">\<|USER_TOKEN|></span><span class="orange-text "> What's the biggest penguin in the world? </span>\<|END_OF_TURN_TOKEN|></span>"""
 
 ### Tool outputs
 
-The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text "><results> {TOOL_OUTPUTS}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
+The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> {TOOL_OUTPUTS}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
 
 > <span class="dark-red-text ">rendered_tool_outputs</span> =
 >
-> <div class="code-block">"""<span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span>  
+> """<span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span>  
 > <span class="dark-red-text">\<results></span>  
 > <span class="dark-red-text">Document: 0</span>  
 > <span class="dark-red-text">Tall penguins</span>  
@@ -291,7 +183,7 @@ The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TU
 > <span class="dark-red-text">Document: 1</span>  
 > <span class="dark-red-text">Penguin habitats</span>  
 > <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
-> <span class="dark-red-text">\</results></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>"""</div>
+> <span class="dark-red-text">\</results\></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>"""
 
 Each tool output should start with <span class="dark-red-text ">Document: {n}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
 
@@ -314,7 +206,7 @@ Next let’s go into a bit more detail about the different components and sectio
 
 #### Preamble Sections
 
-> <div class="code-block"><span class="dark-red-text "># Safety Preamble: This will outline the safety instructions to the model to instruct it not to produce harmful outputs.</span>
+> <span class="dark-red-text "># Safety Preamble: This will outline the safety instructions to the model to instruct it not to produce harmful outputs.</span>
 >
 > <span class="dark-green-text "># System Preamble: System specified rules.</span>  
 > <span class="green-text ">## Basic Rules: This outlines how the model should behave in general.</span>
@@ -330,7 +222,7 @@ Next let’s go into a bit more detail about the different components and sectio
 >
 > <span class="bloody-red-text">{TOOL_OUTPUTS}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
 >
-> <span class="dark-pink-text">{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span></div>
+> <span class="dark-pink-text">{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
 
 <br />
 
@@ -342,11 +234,11 @@ The default instructions for augmented generation (such as in the HuggingFace To
 
 > <span class="dark-pink-text ">AUGMENTED_GENERATION_DEFAULT_INSTRUCTIONS</span> =
 >
-> <div class="code-block"><span style="color:#38761d">"Carefully perform the following instructions, in order, starting each with a new line.
+> "Carefully perform the following instructions, in order, starting each with a new line.
 > Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
 > Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'.
 > Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup.
-> Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols \<co: doc> and \</co: doc> to indicate when a fact comes from a document in the search result, e.g \<co: 0>my fact\</co: 0> for a fact from document 0."</span></div>
+> Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols \<co: doc> and \</co: doc> to indicate when a fact comes from a document in the search result, e.g \<co: 0>my fact\</co: 0> for a fact from document 0."
 
 This default instruction will tell the model to generate four things:
 
@@ -357,17 +249,17 @@ This default instruction will tell the model to generate four things:
 
 This will lead the model to produce an output like:
 
-> <div class="code-block"><span style="color:#38761d">Relevant Documents: 0,1
+> Relevant Documents: 0,1
 > Cited Documents: 0,1
 > Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres.
-> Grounded answer: The \<co: 0>Emperor Penguin\</co: 0> is the \<co: 0>tallest\</co: 0> or biggest penguin in the world. It is a bird that \<co: 1>lives only in Antarctica\</co: 1> and \<co: 0>grows to a height of around 122 centimetres.\</co: 0></span></div>
+> Grounded answer: The \<co: 0>Emperor Penguin\</co: 0> is the \<co: 0>tallest\</co: 0> or biggest penguin in the world. It is a bird that \<co: 1>lives only in Antarctica\</co: 1> and \<co: 0>grows to a height of around 122 centimetres.\</co: 0>
 
 We can easily change the output format of the model by modifying the instruction in our prompt. Let’s get rid of the relevant and cited doc outputs as well as the raw answer. For the remaining grounded answer, let’s change the citation format to be brackets.
 
 > <span class="dark-pink-text ">MODIFIED_AUGMENTED_GENERATION_INSTRUCTIONS</span> =
 >
-> <div class="code-block"><span style="color:#38761d">"Carefully perform the following instructions, in order, starting each with a new line.
-> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact [0] for a fact from document 0."</span></div>
+> "Carefully perform the following instructions, in order, starting each with a new line.
+> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact [0] for a fact from document 0."
 
 This will have the model output something like this:
 
@@ -379,8 +271,8 @@ In addition to changing the format of the output, we can also easily change the
 
 > <span class="dark-sangria-text ">MODIFIED_STYLE_GUIDE</span> =
 >
-> <div class="code-block"><span style="color:#38761d">“””## Style Guide
-> Answer in the style of David Attenborough.”””</span></div>
+> """## Style Guide
+> Answer in the style of David Attenborough."""
 
 Which will have the model instead produce this majestic response:
 
@@ -406,7 +298,7 @@ Starting from our augmented generation prompt, we can improve it a bit by changi
 
 Let’s switch back to our original `STYLE_GUIDE` and see what the fully rendered prompt looks like:
 
-> <div class="code-block"><span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
+> <span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
 > <span class="red-text">The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.</span>
 >
 > <span class="dark-green-text"># System Preamble</span>  
@@ -426,13 +318,13 @@ Let’s switch back to our original `STYLE_GUIDE` and see what the fully rendere
 > <span class="dark-red-text">\<results></span>  
 > <span class="dark-red-text">Document: 0</span>  
 > <span class="dark-red-text">Tall penguins</span>  
-> <span class="dark-red-text">Emperor penguins are the tallest growing up to 122 cm in height.\</span>
+> <span class="dark-red-text">Emperor penguins are the tallest growing up to 122 cm in height.</span>
 >
 > <span class="dark-red-text">Document: 1</span>  
 > <span class="dark-red-text">Penguin habitats</span>  
 > <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
-> <span class="dark-red-text">\</results></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span></span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span class="dark-pink-text ">Carefully perform the following instructions, in order, starting each with a new line.  
-> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact[0] for a fact from document 0.</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span></div>
+> <span class="dark-red-text">\</results></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span>Carefully perform the following instructions, in order, starting each with a new line.  
+> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact[0] for a fact from document 0.<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>
 
 And this has the model output:
 
diff --git a/fern/v1.yml b/fern/v1.yml
index a7ba79bf..8c12f4e1 100644
--- a/fern/v1.yml
+++ b/fern/v1.yml
@@ -420,6 +420,9 @@ navigation:
       - page: Prompt Engineering
         hidden: true
         path: pages/llm-university/intro-text-generation/model-prompting.mdx
+      - page: Prompting Command R
+        hidden: true
+        path: pages/text-generation/prompt-engineering/prompting-command-r.mdx
       - page: Conclusion
         hidden: true
         path: pages/llm-university/intro-text-generation/text-generation-conclusion.mdx
diff --git a/fern/v2.yml b/fern/v2.yml
index a62f8284..c893996e 100644
--- a/fern/v2.yml
+++ b/fern/v2.yml
@@ -420,6 +420,9 @@ navigation:
       - page: Prompt Engineering
         hidden: true
         path: pages/llm-university/intro-text-generation/model-prompting.mdx
+      - page: Prompting Command R
+        hidden: true
+        path: pages/text-generation/prompt-engineering/prompting-command-r.mdx
       - page: Conclusion
         hidden: true
         path: pages/llm-university/intro-text-generation/text-generation-conclusion.mdx

From ddad43cb616b7a7b590ea1705e381ea13cfccc91 Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Tue, 20 Aug 2024 12:21:55 -0400
Subject: [PATCH 3/6] brace fix

---
 .../prompting-command-r.mdx                   | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
index 9e78e21f..82b876e5 100644
--- a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
+++ b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
@@ -101,34 +101,34 @@ The fully rendered prompt above contains a lot of information but it is actually
 > tool_prompt_template =
 >
 > """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">{SAFETY_PREAMBLE}</span>
+> <span class="red-text">\{SAFETY_PREAMBLE\}</span>
 >
 > <br />
 >
 > <span class="dark-green-text"># System Preamble</span>
 >
 > <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">{BASIC_RULES} </span>
+> <span class="green-text">\{BASIC_RULES\}</span>
 >
 > <br />
 >
 > <span class="dark-purple-text"># User Preamble</span>
 >
 > <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">{TASK_CONTEXT}</span>
+> <span class="purple-text">\{TASK_CONTEXT\}</span>
 >
 > <br />
 >
 > <span class="dark-sangria-text">## Style Guide</span>
 >
-> <span class="dark-sangria-text">{STYLE_GUIDE}</span>
+> <span class="dark-sangria-text">\{STYLE_GUIDE\}</span>
 >
 > <br />
 >
 > <span class="magenta-text">## Available Tools</span>
 >
-> <span class="magenta-text">{TOOLS}</span>  
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> <span class="magenta-text">\{TOOLS\}</span>  
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY\} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS\}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 We can see that the prompt is set up in a structured way where we have sections for things like the <span class="green-text ">basic rules</span> we want the model to follow, the <span class="purple-text">task</span> we want it to solve, and the <span class="magenta-text">style</span> in which it should write its output in.
 
@@ -137,22 +137,22 @@ We will take a closer look at these sections later but first lets see how the te
 > augmented_gen_prompt_template =
 >
 > """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">{SAFETY_PREAMBLE}</span>
+> <span class="red-text">\{SAFETY_PREAMBLE\}</span>
 >
 > <span class="dark-green-text"># System Preamble</span>  
 > <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">{BASIC_RULES} </span>
+> <span class="green-text">\{BASIC_RULES\} </span>
 >
 > <span class="dark-purple-text"># User Preamble</span>  
 > <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">{TASK_CONTEXT}</span>
+> <span class="purple-text">\{TASK_CONTEXT\}</span>
 >
 > <span class="dark-sangria-text">## Style Guide</span>  
-> <span class="dark-sangria-text">{STYLE_GUIDE}</span>
+> <span class="dark-sangria-text">\{STYLE_GUIDE\}</span>
 >
 > <span class="yellow-highlight"> </span>
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> {TOOL_OUTPUTS}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY\} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> \{TOOL_OUTPUTS\}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS\}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 Here we can see that the overall structure of the template is very similar between tool use and augmented generation. There are, however, two spots that differ which are highlighted. The first is that we have removed the tool definitions which come after the style guide (you can see that there's no longer an <span class="magenta-text">## Available Tools</span> section), and the second is that we’ve added the <span style="color:#a31515;background-color:#ffff00;">TOOL_OUTPUTS</span> retrieved from search after the chat history.
 
@@ -170,7 +170,7 @@ The rendered chat history is quite simple and the only thing to note is that eac
 
 ### Tool outputs
 
-The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> {TOOL_OUTPUTS}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
+The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> \{TOOL_OUTPUTS\}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
 
 > <span class="dark-red-text ">rendered_tool_outputs</span> =
 >
@@ -185,7 +185,7 @@ The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TU
 > <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
 > <span class="dark-red-text">\</results\></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>"""
 
-Each tool output should start with <span class="dark-red-text ">Document: {n}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
+Each tool output should start with <span class="dark-red-text ">Document: \{n\}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
 
 ## Changing sections of the Prompt
 
@@ -218,11 +218,11 @@ Next let’s go into a bit more detail about the different components and sectio
 >
 > <span class="dark-magenta-text">## Available Tools: If applicable, this will contain definitions of the tools available to the model to use.</span>
 >
-> <span class="orange-text">{CHAT_HISTORY}: This will contain the current dialogue so far and include user queries plus any responses from the model. </span>
+> <span class="orange-text">\{CHAT_HISTORY\}: This will contain the current dialogue so far and include user queries plus any responses from the model. </span>
 >
-> <span class="bloody-red-text">{TOOL_OUTPUTS}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
+> <span class="bloody-red-text">\{TOOL_OUTPUTS\}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
 >
-> <span class="dark-pink-text">{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
+> <span class="dark-pink-text">\{INSTRUCTIONS\}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
 
 <br />
 

From 695e4330fda9de8bb74c682f89211eb3a4d55f87 Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Tue, 20 Aug 2024 12:24:14 -0400
Subject: [PATCH 4/6] another brace fix

---
 .../prompting-command-r.mdx                   | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
index 82b876e5..852e2aae 100644
--- a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
+++ b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
@@ -101,34 +101,34 @@ The fully rendered prompt above contains a lot of information but it is actually
 > tool_prompt_template =
 >
 > """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">\{SAFETY_PREAMBLE\}</span>
+> <span class="red-text">\{SAFETY_PREAMBLE}</span>
 >
 > <br />
 >
 > <span class="dark-green-text"># System Preamble</span>
 >
 > <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">\{BASIC_RULES\}</span>
+> <span class="green-text">\{BASIC_RULES}</span>
 >
 > <br />
 >
 > <span class="dark-purple-text"># User Preamble</span>
 >
 > <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">\{TASK_CONTEXT\}</span>
+> <span class="purple-text">\{TASK_CONTEXT}</span>
 >
 > <br />
 >
 > <span class="dark-sangria-text">## Style Guide</span>
 >
-> <span class="dark-sangria-text">\{STYLE_GUIDE\}</span>
+> <span class="dark-sangria-text">\{STYLE_GUIDE}</span>
 >
 > <br />
 >
 > <span class="magenta-text">## Available Tools</span>
 >
-> <span class="magenta-text">\{TOOLS\}</span>  
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY\} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS\}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> <span class="magenta-text">\{TOOLS}</span>  
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 We can see that the prompt is set up in a structured way where we have sections for things like the <span class="green-text ">basic rules</span> we want the model to follow, the <span class="purple-text">task</span> we want it to solve, and the <span class="magenta-text">style</span> in which it should write its output in.
 
@@ -137,22 +137,22 @@ We will take a closer look at these sections later but first lets see how the te
 > augmented_gen_prompt_template =
 >
 > """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">\{SAFETY_PREAMBLE\}</span>
+> <span class="red-text">\{SAFETY_PREAMBLE}</span>
 >
 > <span class="dark-green-text"># System Preamble</span>  
 > <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">\{BASIC_RULES\} </span>
+> <span class="green-text">\{BASIC_RULES} </span>
 >
 > <span class="dark-purple-text"># User Preamble</span>  
 > <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">\{TASK_CONTEXT\}</span>
+> <span class="purple-text">\{TASK_CONTEXT}</span>
 >
 > <span class="dark-sangria-text">## Style Guide</span>  
-> <span class="dark-sangria-text">\{STYLE_GUIDE\}</span>
+> <span class="dark-sangria-text">\{STYLE_GUIDE}</span>
 >
 > <span class="yellow-highlight"> </span>
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY\} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> \{TOOL_OUTPUTS\}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS\}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> \{TOOL_OUTPUTS}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 Here we can see that the overall structure of the template is very similar between tool use and augmented generation. There are, however, two spots that differ which are highlighted. The first is that we have removed the tool definitions which come after the style guide (you can see that there's no longer an <span class="magenta-text">## Available Tools</span> section), and the second is that we’ve added the <span style="color:#a31515;background-color:#ffff00;">TOOL_OUTPUTS</span> retrieved from search after the chat history.
 
@@ -170,7 +170,7 @@ The rendered chat history is quite simple and the only thing to note is that eac
 
 ### Tool outputs
 
-The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> \{TOOL_OUTPUTS\}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
+The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> \{TOOL_OUTPUTS}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
 
 > <span class="dark-red-text ">rendered_tool_outputs</span> =
 >
@@ -185,7 +185,7 @@ The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TU
 > <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
 > <span class="dark-red-text">\</results\></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>"""
 
-Each tool output should start with <span class="dark-red-text ">Document: \{n\}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
+Each tool output should start with <span class="dark-red-text ">Document: \{n}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
 
 ## Changing sections of the Prompt
 
@@ -218,11 +218,11 @@ Next let’s go into a bit more detail about the different components and sectio
 >
 > <span class="dark-magenta-text">## Available Tools: If applicable, this will contain definitions of the tools available to the model to use.</span>
 >
-> <span class="orange-text">\{CHAT_HISTORY\}: This will contain the current dialogue so far and include user queries plus any responses from the model. </span>
+> <span class="orange-text">\{CHAT_HISTORY}: This will contain the current dialogue so far and include user queries plus any responses from the model. </span>
 >
-> <span class="bloody-red-text">\{TOOL_OUTPUTS\}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
+> <span class="bloody-red-text">\{TOOL_OUTPUTS}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
 >
-> <span class="dark-pink-text">\{INSTRUCTIONS\}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
+> <span class="dark-pink-text">\{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
 
 <br />
 

From 3a5c3e63055d7fecc00aedbabd613a06cba724d2 Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Tue, 20 Aug 2024 12:28:54 -0400
Subject: [PATCH 5/6] missed one

---
 .../text-generation/prompt-engineering/prompting-command-r.mdx  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
index 852e2aae..3b79ba33 100644
--- a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
+++ b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
@@ -128,7 +128,7 @@ The fully rendered prompt above contains a lot of information but it is actually
 > <span class="magenta-text">## Available Tools</span>
 >
 > <span class="magenta-text">\{TOOLS}</span>  
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> {CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
 
 We can see that the prompt is set up in a structured way where we have sections for things like the <span class="green-text ">basic rules</span> we want the model to follow, the <span class="purple-text">task</span> we want it to solve, and the <span class="magenta-text">style</span> in which it should write its output in.
 

From d7b431c442d0a0b3b8a74e98e16882672b4f203b Mon Sep 17 00:00:00 2001
From: fern-bot <info@buildwithfern.com>
Date: Tue, 20 Aug 2024 12:43:20 -0400
Subject: [PATCH 6/6] remove html

---
 .../prompting-command-r.mdx                   | 228 +++++++++---------
 1 file changed, 115 insertions(+), 113 deletions(-)

diff --git a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
index 3b79ba33..f45e449c 100644
--- a/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
+++ b/fern/pages/text-generation/prompt-engineering/prompting-command-r.mdx
@@ -17,7 +17,7 @@ The easiest way to make sure your prompts will work well with Command R is to us
 
 ## Structured Prompts for RAG
 
-Before going into detail on the different components of the prompt and how they fit together, let’s start by looking at a fully rendered prompt. Let’s take an example of using Command R for a simple RAG use case where we are given a user query like: <span class="orange-text">What’s the biggest penguin in the world?</span>
+Before going into detail on the different components of the prompt and how they fit together, let’s start by looking at a fully rendered prompt. Let’s take an example of using Command R for a simple RAG use case where we are given a user query like: What’s the biggest penguin in the world?
 
 To solve this problem, we will use the model to perform the two steps of RAG:
 
@@ -26,43 +26,45 @@ To solve this problem, we will use the model to perform the two steps of RAG:
 
 ### Fully Rendered Default Tool-use Prompt
 
-Let’s start with retrieval, where the model will make calls to an <span class="quartz-text ">internet_search</span> tool to collect relevant documents needed to answer the user’s question. To enable that, we will create a rendered tool use prompt that will give the model access to two tools:
+Let’s start with retrieval, where the model will make calls to an internet_search tool to collect relevant documents needed to answer the user’s question. To enable that, we will create a rendered tool use prompt that will give the model access to two tools:
 
-- <span class="quartz-text ">def internet_search(query: str)</span>
-- <span class="quartz-text ">def directly_answer()</span>
+- def internet_search(query: str)
+- def directly_answer()
 
 Let's take a look at what this fully rendered prompt would look like using our default settings.
 
-Note that you could get the same result if you were using the HuggingFace Tokenizer’s <span class="blue-text ">apply_tool_use_template</span> and setting the <span class="orange-text ">conversation</span> and <span class="quartz-text ">tools</span> parameters.
+Note that you could get the same result if you were using the HuggingFace Tokenizer’s apply_tool_use_template and setting the conversation and tools parameters.
 
 > tool_use_prompt =
 >
-> <div class="code-block">"""<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text ">The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.</span></div>
+> """\<BOS_TOKEN>\<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> # Safety Preamble
+> The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
 >
 > <br />
 >
-> <span class="dark-green-text"># System Preamble</span>
+> # System Preamble
 >
-> <div class="code-block"><span class="green-text">## Basic Rules</span><span class="green-text">You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. </span></div>
+> ## Basic Rules
+> You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. 
 >
 > <br />
 >
-> <span class="dark-purple-text"># User Preamble</span>
+> # User Preamble
 >
-> <div class="code-block"><span class="purple-text">## Task and Context</span><span class="purple-text">You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.</span></div>
+> ## Task and Context
+> You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
 >
 > <br />
 >
-> <span class="dark-sangria-text">## Style Guide</span>
+> ## Style Guide
 >
-> <div class="code-block"><span class="dark-sangria-text">Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.</span></div>
+> Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
 >
 > <br />
 >
-> <span class="magenta-text">## Available Tools</span>
+> ## Available Tools
 >
-> <span class="magenta-text">Here is a list of tools that you have available to you:</span>
+> Here is a list of tools that you have available to you:
 > ```python PYTHON
 > def internet_search(query: str) -> List[Dict]:
 >      """Returns a list of relevant document snippets for a textual query retrieved from the internet
@@ -70,18 +72,18 @@ Note that you could get the same result if you were using the HuggingFace Tokeni
 >          query (str): Query to search the internet with
 >      """
 >      pass
-> ```</span></pre>
+> ```
 >
-> <pre><span class="magenta-text">```python PYTHON
+> ```python PYTHON
 > def directly_answer() -> List[Dict]:
 >     """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
 >     """
 >     pass
 > ```
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|USER_TOKEN|></span>  
-> <span class="orange-text "> What's the biggest penguin in the world?</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span>  
-> <span class="magenta-text">Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the \`directly-answer\` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:</span>
+> \<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|USER_TOKEN|>
+> What's the biggest penguin in the world?\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> 
+> Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the \`directly-answer\` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
 >
 > ```json JSON
 > [
@@ -92,7 +94,7 @@ Note that you could get the same result if you were using the HuggingFace Tokeni
 > ]
 > ```
 >
-> <span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> \<|END_OF_TURN_TOKEN|>"""
 
 ### Structured Prompt Templates
 
@@ -100,61 +102,61 @@ The fully rendered prompt above contains a lot of information but it is actually
 
 > tool_prompt_template =
 >
-> """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">\{SAFETY_PREAMBLE}</span>
+> """\<BOS_TOKEN> \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> # Safety Preamble 
+> \{SAFETY_PREAMBLE}
 >
 > <br />
 >
-> <span class="dark-green-text"># System Preamble</span>
+> # System Preamble
 >
-> <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">\{BASIC_RULES}</span>
+> ## Basic Rules
+> \{BASIC_RULES}
 >
 > <br />
 >
-> <span class="dark-purple-text"># User Preamble</span>
+> # User Preamble
 >
-> <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">\{TASK_CONTEXT}</span>
+> ## Task and Context  
+> \{TASK_CONTEXT}
 >
 > <br />
 >
-> <span class="dark-sangria-text">## Style Guide</span>
+> ## Style Guide
 >
-> <span class="dark-sangria-text">\{STYLE_GUIDE}</span>
+> \{STYLE_GUIDE}
 >
 > <br />
 >
-> <span class="magenta-text">## Available Tools</span>
+> ## Available Tools
 >
-> <span class="magenta-text">\{TOOLS}</span>  
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> \{TOOLS}  
+> \<|END_OF_TURN_TOKEN|>  \{CHAT_HISTORY} \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> \{INSTRUCTIONS}\<|END_OF_TURN_TOKEN|>"""
 
-We can see that the prompt is set up in a structured way where we have sections for things like the <span class="green-text ">basic rules</span> we want the model to follow, the <span class="purple-text">task</span> we want it to solve, and the <span class="magenta-text">style</span> in which it should write its output in.
+We can see that the prompt is set up in a structured way where we have sections for things like the basic rules we want the model to follow, the task we want it to solve, and the style in which it should write its output in.
 
 We will take a closer look at these sections later but first lets see how the template changes when we shift from tool use to the second stage of RAG: augmented generation.
 
 > augmented_gen_prompt_template =
 >
-> """<span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">\{SAFETY_PREAMBLE}</span>
+> """\<BOS_TOKEN> \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> # Safety Preamble
+> \{SAFETY_PREAMBLE}
 >
-> <span class="dark-green-text"># System Preamble</span>  
-> <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">\{BASIC_RULES} </span>
+> # System Preamble  
+> ## Basic Rules  
+> \{BASIC_RULES} 
 >
-> <span class="dark-purple-text"># User Preamble</span>  
-> <span class="purple-text">## Task and Context</span>  
-> <span class="purple-text">\{TASK_CONTEXT}</span>
+> # User Preamble  
+> ## Task and Context  
+> \{TASK_CONTEXT}
 >
-> <span class="dark-sangria-text">## Style Guide</span>  
-> <span class="dark-sangria-text">\{STYLE_GUIDE}</span>
+> ## Style Guide  
+> \{STYLE_GUIDE}
 >
-> <span class="yellow-highlight"> </span>
+>  
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="orange-text "> \{CHAT_HISTORY} </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span><span style="color:#a31515;background-color:#ffff00;"> \{TOOL_OUTPUTS}</span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span> <span class="magenta-text">\{INSTRUCTIONS}</span><span class="brown-text">\<|END_OF_TURN_TOKEN|></span>"""
+> \<|END_OF_TURN_TOKEN|>  \{CHAT_HISTORY} \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> \{TOOL_OUTPUTS}\<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> \{INSTRUCTIONS}\<|END_OF_TURN_TOKEN|>"""
 
-Here we can see that the overall structure of the template is very similar between tool use and augmented generation. There are, however, two spots that differ which are highlighted. The first is that we have removed the tool definitions which come after the style guide (you can see that there's no longer an <span class="magenta-text">## Available Tools</span> section), and the second is that we’ve added the <span style="color:#a31515;background-color:#ffff00;">TOOL_OUTPUTS</span> retrieved from search after the chat history.
+Here we can see that the overall structure of the template is very similar between tool use and augmented generation. There are, however, two spots that differ which are highlighted. The first is that we have removed the tool definitions which come after the style guide (you can see that there's no longer an ## Available Tools section), and the second is that we’ve added the TOOL_OUTPUTS retrieved from search after the chat history.
 
 ## Formatting Chat History and Tool Outputs
 
@@ -162,30 +164,30 @@ For our augmented generation call we will need to render both our chat history a
 
 ### Chat History
 
-The rendered chat history is quite simple and the only thing to note is that each turn of the conversation should begin with a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span> followed by one of <span class="dark-orange-text ">\<|USER_TOKEN|></span>, <span class="dark-orange-text ">\<|CHATBOT_TOKEN|></span>, or <span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span>(depending on the role of the speaker), and finally <span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>.
+The rendered chat history is quite simple and the only thing to note is that each turn of the conversation should begin with a \<|START_OF_TURN_TOKEN|> followed by one of \<|USER_TOKEN|>, \<|CHATBOT_TOKEN|>, or \<|SYSTEM_TOKEN|> (depending on the role of the speaker), and finally \<|END_OF_TURN_TOKEN|>.
 
-> <span class="orange-text ">rendered_chat_history</span> =
+> rendered_chat_history =
 >
-> """<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="brown-text "><span class="dark-orange-text ">\<|USER_TOKEN|></span><span class="orange-text "> What's the biggest penguin in the world? </span>\<|END_OF_TURN_TOKEN|></span>"""
+> """\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|USER_TOKEN|> What's the biggest penguin in the world? \<|END_OF_TURN_TOKEN|>"""
 
 ### Tool outputs
 
-The tool outputs should be wrapped in a <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span><span class="dark-red-text ">\<results> \{TOOL_OUTPUTS}</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> and look something like:
+The tool outputs should be wrapped in a \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|>\<results> \{TOOL_OUTPUTS}\<|END_OF_TURN_TOKEN|> and look something like:
 
-> <span class="dark-red-text ">rendered_tool_outputs</span> =
+> rendered_tool_outputs =
 >
-> """<span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text">\<|SYSTEM_TOKEN|></span>  
-> <span class="dark-red-text">\<results></span>  
-> <span class="dark-red-text">Document: 0</span>  
-> <span class="dark-red-text">Tall penguins</span>  
-> <span class="dark-red-text">Emperor penguins are the tallest growing up to 122 cm in height.</span>\*\*
+> """\<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|>  
+> \<results>  
+> Document: 0  
+> Tall penguins  
+> Emperor penguins are the tallest growing up to 122 cm in height.\*\*
 >
-> <span class="dark-red-text">Document: 1</span>  
-> <span class="dark-red-text">Penguin habitats</span>  
-> <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
-> <span class="dark-red-text">\</results\></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>"""
+> Document: 1  
+> Penguin habitats  
+> Emperor penguins only live in Antarctica.  
+> \</results\>\<|END_OF_TURN_TOKEN|>"""
 
-Each tool output should start with <span class="dark-red-text ">Document: \{n}</span> and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
+Each tool output should start with Document: \{n} and should be an ascending list of integers starting at 0. You can put all kinds of different things as a tool output. In our example, the tool outputs are simple key-value string-string pairs. In general keys should be relatively short descriptive strings, but values can have a lot of variety - e.g. markdown tables or json.
 
 ## Changing sections of the Prompt
 
@@ -197,32 +199,32 @@ Next let’s go into a bit more detail about the different components and sectio
 
 #### Special Tokens
 
-- <span class="dark-blue-text ">`<BOS_TOKEN>`: This is a special token used by Command R models to signify the beginning of a prompt. When using raw_prompting, you should always start with this token.</span>
-- <span class="brown-text ">`<|START_OF_TURN_TOKEN|>`: This special token is used at the beginning of something said by either the USER, SYSTEM, or CHATBOT.</span>
-- <span class="dark-orange-text ">`<|USER_TOKEN|>`: This should immediately follow `<START_OF_TURN_TOKEN>` and signifies that the following output is meant to be from the user such as a query.</span>
-- <span class="dark-orange-text ">`<|SYSTEM_TOKEN|>`: Same as the `USER` token but indicating some system instruction.</span>
-- <span class="dark-orange-text ">`<|CHATBOT_TOKEN|>`: same as `USER` and `SYSTEM` token but indicating a chatbot output.</span>
-- <span class="brown-text ">`<|END_OF_TURN_TOKEN|>`: This will immediately follow the content of a `USER`, `CHATBOT`, or `SYSTEM` turn.</span>
+- `<BOS_TOKEN>`: This is a special token used by Command R models to signify the beginning of a prompt. When using raw_prompting, you should always start with this token.
+- `<|START_OF_TURN_TOKEN|>`: This special token is used at the beginning of something said by either the USER, SYSTEM, or CHATBOT.
+- `<|USER_TOKEN|>`: This should immediately follow `<START_OF_TURN_TOKEN>` and signifies that the following output is meant to be from the user such as a query.
+- `<|SYSTEM_TOKEN|>`: Same as the `USER` token but indicating some system instruction.
+- `<|CHATBOT_TOKEN|>`: same as `USER` and `SYSTEM` token but indicating a chatbot output.
+- `<|END_OF_TURN_TOKEN|>`: This will immediately follow the content of a `USER`, `CHATBOT`, or `SYSTEM` turn.
 
 #### Preamble Sections
 
-> <span class="dark-red-text "># Safety Preamble: This will outline the safety instructions to the model to instruct it not to produce harmful outputs.</span>
+> # Safety Preamble: This will outline the safety instructions to the model to instruct it not to produce harmful outputs.
 >
-> <span class="dark-green-text "># System Preamble: System specified rules.</span>  
-> <span class="green-text ">## Basic Rules: This outlines how the model should behave in general.</span>
+> # System Preamble: System specified rules.  
+> ## Basic Rules: This outlines how the model should behave in general.
 >
-> <span class="dark-purple-text"># User Preamble: User specified rules.</span>  
-> <span class="purple-text">## Task and Context: Here we outline the specific task it is that we want the model to solve and any additional required context.</span>
+> # User Preamble: User specified rules.  
+> ## Task and Context: Here we outline the specific task it is that we want the model to solve and any additional required context.
 >
-> <span class="dark-sangria-text">## Style Guide: Here we tell the model what the output should look like for example ‘respond in full sentences’ or ‘respond like a pirate’.</span>
+> ## Style Guide: Here we tell the model what the output should look like for example ‘respond in full sentences’ or ‘respond like a pirate’.
 >
-> <span class="dark-magenta-text">## Available Tools: If applicable, this will contain definitions of the tools available to the model to use.</span>
+> ## Available Tools: If applicable, this will contain definitions of the tools available to the model to use.
 >
-> <span class="orange-text">\{CHAT_HISTORY}: This will contain the current dialogue so far and include user queries plus any responses from the model. </span>
+> \{CHAT_HISTORY}: This will contain the current dialogue so far and include user queries plus any responses from the model. 
 >
-> <span class="bloody-red-text">\{TOOL_OUTPUTS}: This is where we would add any rendered tool outputs, such as returned documents from a search.</span>
+> \{TOOL_OUTPUTS}: This is where we would add any rendered tool outputs, such as returned documents from a search.
 >
-> <span class="dark-pink-text">\{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.</span>
+> \{INSTRUCTIONS}: These are the specific instructions that the model should follow when producing its output. For example, we could tell the model that it should produce a tool function call in a particular format, or for augmented generation, we could tell the model to generate an answer along with citations.
 
 <br />
 
@@ -230,9 +232,9 @@ Now that we’ve looked at a high level of the structured prompt and what each o
 
 ### Changing the Output Format: Citation Style
 
-The default instructions for augmented generation (such as in the HuggingFace Tokenizer) uses the following <span class="dark-pink-text">INSTRUCTIONS</span>:
+The default instructions for augmented generation (such as in the HuggingFace Tokenizer) uses the following INSTRUCTIONS:
 
-> <span class="dark-pink-text ">AUGMENTED_GENERATION_DEFAULT_INSTRUCTIONS</span> =
+> AUGMENTED_GENERATION_DEFAULT_INSTRUCTIONS =
 >
 > "Carefully perform the following instructions, in order, starting each with a new line.
 > Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
@@ -245,7 +247,7 @@ This default instruction will tell the model to generate four things:
 1. A list of docs relevant to the query.
 2. A list of docs that will be cited in the answer.
 3. A plain text answer to the question
-4. A grounded answer which includes citations with the format <span class="grass-green-text">`<co: 0>my fact</co: 0>`</span>.
+4. A grounded answer which includes citations with the format `<co: 0>my fact</co: 0>`.
 
 This will lead the model to produce an output like:
 
@@ -256,27 +258,27 @@ This will lead the model to produce an output like:
 
 We can easily change the output format of the model by modifying the instruction in our prompt. Let’s get rid of the relevant and cited doc outputs as well as the raw answer. For the remaining grounded answer, let’s change the citation format to be brackets.
 
-> <span class="dark-pink-text ">MODIFIED_AUGMENTED_GENERATION_INSTRUCTIONS</span> =
+> MODIFIED_AUGMENTED_GENERATION_INSTRUCTIONS =
 >
 > "Carefully perform the following instructions, in order, starting each with a new line.
 > Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact [0] for a fact from document 0."
 
 This will have the model output something like this:
 
-> <div class="code-block"><span style="color:#38761d">Grounded answer: The Emperor Penguin[0] is the tallest[0] or biggest penguin in the world. It is a bird that lives only in Antarctica[1] and grows to a height of around 122 centimetres.[0]</span></div>
+> Grounded answer: The Emperor Penguin[0] is the tallest[0] or biggest penguin in the world. It is a bird that lives only in Antarctica[1] and grows to a height of around 122 centimetres.[0]
 
 ### Changing the Style
 
-In addition to changing the format of the output, we can also easily change the style by modifying the <span class="dark-sangria-text">STYLE_GUIDE</span>.
+In addition to changing the format of the output, we can also easily change the style by modifying the STYLE_GUIDE.
 
-> <span class="dark-sangria-text ">MODIFIED_STYLE_GUIDE</span> =
+> MODIFIED_STYLE_GUIDE =
 >
 > """## Style Guide
 > Answer in the style of David Attenborough."""
 
 Which will have the model instead produce this majestic response:
 
-> <div class="code-block"><span style="color:#38761d">Grounded answer: And here, emerging from the icy waters, is the majestic emperor penguin, the largest species of its kind. Growing to an impressive height of 122 centimeters[0], these majestic birds rule the Antarctic[1] oceans. Their imposing stature and proud demeanor make them a sight to behold.</span></div>
+> Grounded answer: And here, emerging from the icy waters, is the majestic emperor penguin, the largest species of its kind. Growing to an impressive height of 122 centimeters[0], these majestic birds rule the Antarctic[1] oceans. Their imposing stature and proud demeanor make them a sight to behold.
 
 Inspiring.
 
@@ -286,49 +288,49 @@ Grounded summarization is very similar to augmented generation; in both settings
 
 In grounded summarization, as the name implies, we instead want to create a summary of those documents. Additionally, unlike a set of search results, the order of the document chunks that the model receives actually matters.
 
-Starting from our augmented generation prompt, we can improve it a bit by changing the <span class="dark-purple-text">TASK_CONTEXT</span> to better fit what we want the model to do.
+Starting from our augmented generation prompt, we can improve it a bit by changing the TASK_CONTEXT to better fit what we want the model to do.
 
-> <span class="dark-purple-text ">TASK_CONTEXT</span> =
+> TASK_CONTEXT =
 >
-> <div class="code-block"><span style="color:#38761d">"You will receive a series of text fragments from an article that are presented in chronological order. As the assistant, you must generate responses to user's requests based on the information given in the fragments. Ensure that your responses are accurate and truthful, and that you reference your sources where appropriate to answer the queries, regardless of their complexity."</span></div>
+> "You will receive a series of text fragments from an article that are presented in chronological order. As the assistant, you must generate responses to user's requests based on the information given in the fragments. Ensure that your responses are accurate and truthful, and that you reference your sources where appropriate to answer the queries, regardless of their complexity."
 >
-> <span class="orange-text ">CHAT_HISTORY</span> =
+> CHAT_HISTORY =
 >
-> <div class="code-block">"<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span> <span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="brown-text "><span class="dark-orange-text ">\<|USER_TOKEN|></span><span class="orange-text "> Summarize the documents in 20 words or less. </span>\<|END_OF_TURN_TOKEN|></span>"</div>
+> "\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|USER_TOKEN|> Summarize the documents in 20 words or less. \<|END_OF_TURN_TOKEN|>"
 
 Let’s switch back to our original `STYLE_GUIDE` and see what the fully rendered prompt looks like:
 
-> <span class="dark-blue-text ">\<BOS_TOKEN> </span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|> </span><span class="red-text"># Safety Preamble</span>
-> <span class="red-text">The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.</span>
+> \<BOS_TOKEN> \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|> # Safety Preamble
+> The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
 >
-> <span class="dark-green-text"># System Preamble</span>  
-> <span class="green-text">## Basic Rules</span>  
-> <span class="green-text">You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. </span>
+> # System Preamble  
+> ## Basic Rules  
+> You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. 
 >
-> <span class="dark-purple-text"># User Preamble</span>  
-> <span class="purple-text">## Task and Context</span>
+> # User Preamble  
+> ## Task and Context
 >
-> <span class="purple-text">You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.</span>
+> You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
 >
-> <span class="dark-sangria-text">## Style Guide</span>  
-> <span class="dark-sangria-text">Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.</span>
+> ## Style Guide  
+> Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
 >
-> <span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|USER_TOKEN|></span><span class="orange-text "> Summarize the documents in 20 words or less.</span><span class="brown-text ">\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span>
+> \<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|USER_TOKEN|> Summarize the documents in 20 words or less.\<|END_OF_TURN_TOKEN|> \<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|>
 >
-> <span class="dark-red-text">\<results></span>  
-> <span class="dark-red-text">Document: 0</span>  
-> <span class="dark-red-text">Tall penguins</span>  
-> <span class="dark-red-text">Emperor penguins are the tallest growing up to 122 cm in height.</span>
+> \<results>  
+> Document: 0  
+> Tall penguins  
+> Emperor penguins are the tallest growing up to 122 cm in height.
 >
-> <span class="dark-red-text">Document: 1</span>  
-> <span class="dark-red-text">Penguin habitats</span>  
-> <span class="dark-red-text">Emperor penguins only live in Antarctica.</span>  
-> <span class="dark-red-text">\</results></span><span class="brown-text ">\<|END_OF_TURN_TOKEN|></span><span class="brown-text ">\<|START_OF_TURN_TOKEN|></span><span class="dark-orange-text ">\<|SYSTEM_TOKEN|></span>Carefully perform the following instructions, in order, starting each with a new line.  
-> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact[0] for a fact from document 0.<span class="brown-text ">\<|END_OF_TURN_TOKEN|></span>
+> Document: 1  
+> Penguin habitats  
+> Emperor penguins only live in Antarctica.  
+> \</results>\<|END_OF_TURN_TOKEN|>\<|START_OF_TURN_TOKEN|>\<|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line.  
+> Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbol [doc] to indicate when a fact comes from a document in the search result, e.g my fact[0] for a fact from document 0.\<|END_OF_TURN_TOKEN|>
 
 And this has the model output:
 
-> <div class="code-block"><span style="color:#38761d">Grounded answer: Emperor penguins, the tallest species, grow up to 122 cm tall[0] and only live in Antarctica.[1].</span></div>
+> Grounded answer: Emperor penguins, the tallest species, grow up to 122 cm tall[0] and only live in Antarctica.[1].
 
 ## Appendix