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_posts/2024-08-15-Prompt-Engineering.md

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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/)<br>
+* [Deep Learning Generative AI short courses](https://www.deeplearning.ai/short-courses/)<br>
+![](https://github.com/rkuo2000/AI-course/blob/main/images/Prompting-101.jpg?raw=true)
+
+### [Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)<br>
+aka **In-Context Prompting**<br>
+* 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)
+![](https://github.com/rkuo2000/AI-course/blob/main/images/LLM_prompt_patterns.jpg?raw=true)
+
+**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)<br>
+
+---
+## 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
+<iframe width="863" height="486" src="https://www.youtube.com/embed/A3Yx35KrSN0?list=PLJV_el3uVTsPz6CTopeRp2L2t4aL_KgiI" title="【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" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+---
+### 【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
+<iframe width="863" height="486" src="https://www.youtube.com/embed/lwe3_x50_uw?list=PLJV_el3uVTsPz6CTopeRp2L2t4aL_KgiI" title="【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" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+---
+### 【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
+<iframe width="1271" height="716" src="https://www.youtube.com/embed/inebiWdQW-4" title="【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 (開頭有芙莉蓮雷，慎入)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+---
+## 神奇的咒語
+不是對所有的模型都有用<br>
+![](https://github.com/rkuo2000/AI-course/blob/main/images/prompt_principles_for_instructions.jpg?raw=true)
+![](https://github.com/rkuo2000/AI-course/blob/main/images/chatgpt_cheat_sheet_v2.jpg?raw=true)
+
+### 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)<br>
+![](https://ar5iv.labs.arxiv.org/html/2201.11903/assets/x1.png)
+
+---
+### 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)<br>
+![](https://aisholar.s3.ap-northeast-1.amazonaws.com/posts/July2023/Can_Large_Language_Models_Be_an_Alternative_to_Human_Evaluation_fig1.png)
+
+---
+### 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)<br>
+![](https://digitalrosh.com/wp-content/uploads/2024/01/EmotionPrompt.jpg)
+
+---
+### 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)<br>
+![](https://arxiv.org/html/2312.16171v2/x1.png)
+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**<br>
+
+**Paper:** [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)<br>
+![](https://miro.medium.com/v2/resize:fit:720/format:webp/1*1PJi06R7QMTGBh8CdIsW8w.png)
+
+---
+### 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)<br>
+**Paper:** [Joint Prompt Optimization of Stacked LLMs using Variational Inference](https://arxiv.org/abs/2306.12509)<br>
+
+---
+### 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)<br>
+![](https://github.com/rkuo2000/AI-course/blob/main/images/Prompting_AnyTool.png?raw=true)
+
+---
+
+---
+### 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)<br>
+**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)<br>
+
+---
+### 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**<br>
+* 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
+
+<iframe width="988" height="556" src="https://www.youtube.com/embed/iBWJ5rJ_a6I" title="善用ChatGPT，讓專業的你更亮眼 ! | 生成式AI主題論壇" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>**Paper:** [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073)<br>
+
+---
+## 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)<br>
+
+### [Chain-of-Thought Prompting](https://www.promptingguide.ai/techniques/cot)
+![](https://www.promptingguide.ai/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcot.1933d9fe.png&w=1080&q=75)
+
+1. **Zero-shot COT Prompting**
+![](https://www.promptingguide.ai/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fzero-cot.79793bee.png&w=1080&q=75)
+
+2. **Automatic Chain-of-Thought (Auto-CoT)**
+![](https://www.promptingguide.ai/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fauto-cot.642d9bad.png&w=1200&q=75)
+
+---
+### [Chain of thought and ReAct — SQL Agent](https://abvijaykumar.medium.com/prompt-engineering-chain-of-thought-and-react-sql-agent-85fa42575c06)
+![](https://miro.medium.com/v2/resize:fit:720/format:webp/1*XBh0aKnnFvI5wvpi5LAv4A.png)
+<iframe width="680" height="510" src="https://www.youtube.com/embed/q0SQEIXPE14" title="Demonstration of ReAct in action" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+<br>
+<br>
+
+*This site was last updated {{ site.time | date: "%B %d, %Y" }}.*
+