From d1c1b32faa5884bd096c49aa8552486055a297f0 Mon Sep 17 00:00:00 2001
From: Sharan Shirodkar <91109427+sharanshirodkar7@users.noreply.github.com>
Date: Tue, 23 Apr 2024 10:29:37 -0400
Subject: [PATCH] Update ada.mdx

---
 fern/docs/pages/guides/ada.mdx | 2 --
 1 file changed, 2 deletions(-)

diff --git a/fern/docs/pages/guides/ada.mdx b/fern/docs/pages/guides/ada.mdx
index 1240049..1d44f51 100644
--- a/fern/docs/pages/guides/ada.mdx
+++ b/fern/docs/pages/guides/ada.mdx
@@ -2,8 +2,6 @@
 title: Data Chat with LLMs
 ---
 
-## Using LLMs for Data Analysis and SQL Query Generation
-
 (Run this example in Google Colab [here](https://colab.research.google.com/drive/1RhQpyG9lgXk4aErlcuTOASLsU4zq5IgT?usp=sharing#scrollTo=VTwxTNIFWeZX))
 
 Large language models (LLMs) like 'deepseek-coder-6.7B-instruct' have demonstrated impressive capabilities for understanding natural language and generating SQL. We can leverage these skills for data analysis by having them automatically generate SQL queries against known database structures. And then rephrase these sql outputs using state of the art text/chat completion models like 'Neural-Chat-7B' to get well written answers to user questions.