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+---
+layout: post
+title: Prompt Engineering
+author: [Richard Kuo]
+category: [Lecture]
+tags: [jekyll, ai]
+---
+
+Prompt Engineering (提示工程), Chain-of-Thought Prompting
+
+---
+## 初級入門（免費課程）
+* [Prompt Engineering with Llama 2](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2/)
+* [Deep Learning Generative AI short courses](https://www.deeplearning.ai/short-courses/)
+
+
+### [Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)
+aka **In-Context Prompting**
+* Basic Prompting
+ - Zero-shot
+ - Few-shot
+* Instruction Prompting
+* Self-Consistency Sampling
+* Chain-of-Thought (CoT)
+* Automatic Prompt Design
+* [Augmented Language Models](https://arxiv.org/abs/2302.07842)
+
+
+**Blog:** [LLM 各種技巧 | Prompt Engineering 大總結 | 指南](https://medium.com/@cch.chichieh/llm-%E5%90%84%E7%A8%AE%E6%8A%80%E5%B7%A7-prompt-engineering-%E6%8C%87%E5%8D%97-6ac4201a4cbe)
+
+---
+## 生成式AI導論
+
+### 【生成式AI導論 2024】第3講：訓練不了人工智慧？你可以訓練你自己 (上) — 神奇咒語與提供更多資訊
+
+
+---
+### 【生成式AI導論 2024】第4講：訓練不了人工智慧？你可以訓練你自己 (中) — 拆解問題與使用工具
+
+
+---
+### 【生成式AI導論 2024】第5講：訓練不了人工智慧？你可以訓練你自己 (下) — 讓語言彼此合作，把一個人活成一個團隊
+
+
+---
+## 神奇的咒語
+不是對所有的模型都有用
+
+
+
+### 1. 要語言模型思考: "Think about it Step-by-Step"
+**Paper:** [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903)
+
+
+---
+### 2. 要語言模型解釋: "Explain it"
+**Paper:** [Can Large Language Models Be an Alternative to Human Evaluation?](https://arxiv.org/abs/2305.01937)
+
+
+---
+### 3. 對語言模型情緒勒索: "This is very important to my career"
+**Paper:** [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://arxiv.org/abs/2307.11760)
+
+
+---
+### 4. 對語言模型下原則性指導 （Principled Instructions）
+**Paper:** [Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4](https://arxiv.org/abs/2312.16171)
+
+1. 對它說話不用客氣, 對它有什麼要求直說無妨
+2. 給它明確的指示, 直說要它做甚麼, 不用跟它說不做什麼
+3. 跟它說如果做得好,會給它小費$xxx
+4. 跟它說做不好, 它會得到處罰
+5. 跟它說要保證它的答案是沒有偏見的, 而且要避免使用任何刻板印象
+...
+
+---
+### 5.直接問語言模型給更強大的咒語 (Forward Generation Template)
+* `Let's think step by step`
+* `Take a deep breath then work on this problem step by step`
+
+---
+### 6. 輸入額外範例
+**In-Context Learning**
+
+**Paper:** [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+
+
+---
+### 7. 拆解任務 （Recursive Reprompting and Revision)
+**Paper:** [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://arxiv.org/abs/2210.06774)
+**Paper:** [Joint Prompt Optimization of Stacked LLMs using Variational Inference](https://arxiv.org/abs/2306.12509)
+
+---
+### 8. 要語言模型去使用工具
+**Paper:** [AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls](https://arxiv.org/abs/2402.04253)
+
+
+---
+
+---
+### 9. 自動提示比手工編寫的提示好
+**Paper:** [The Unreasonable Effectiveness of Eccentric Automatic Prompts](https://arxiv.org/html/2402.10949v2)
+**Blog:** [要求AI模仿《星際爭霸戰》能提升效能，但還是用「自動優化」以魔法打敗魔法最有效
+](https://www.techbang.com/posts/113796-asking-the-ai-to-answer-in-the-way-of-a-starcraft-character)
+
+---
+### 10. 要語言模型摘要文章/論文後寫出簡報/文章
+**收集, 啟發, 輸出**
+* 一段一段分析文章中提供了什麼解決方法
+* 如何解決我提出的需求與問題？
+* 根據問題, 詳列摘要重點
+* 改寫成精簡但精采的簡報大綱
+
+**Paper:** [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073)
+
+---
+## Chain-of-Thought Prompting
+**Blog:** [How to Make ChatGPT Smarter: Chain of Thought Prompting](https://medium.com/@wadan/how-to-make-chatgpt-smarter-chain-of-thought-prompting-bd796efa1e03)
+
+### [Chain-of-Thought Prompting](https://www.promptingguide.ai/techniques/cot)
+
+
+1. **Zero-shot COT Prompting**
+
+
+2. **Automatic Chain-of-Thought (Auto-CoT)**
+
+
+---
+### [Chain of thought and ReAct — SQL Agent](https://abvijaykumar.medium.com/prompt-engineering-chain-of-thought-and-react-sql-agent-85fa42575c06)
+
+
+
+
+
+
+*This site was last updated {{ site.time | date: "%B %d, %Y" }}.*
+