Prompt Engineering in Action — 书籍拆解
读到哪:未读。
readState不是 read/partial 的书不能当锚。
| 项 | |
|---|---|
| 作者 | Shivendra Srivastava and Naresh Vurukonda |
| 版次 | 2025 版 |
| 格式 | epub ｜ 文本源 epub-builtin |
| 许可 | 自购/个人收藏 |
| 来源 | 主人个人藏书,2026-08 放入收件箱 |
| 清洗 | 删页眉页脚 0 行、页码 0 行、断词接回 0 处 |
我们重写的拆解(0 章)
（还没写。拆解是这本书对我们的真正产出——底下的元数据只是索引。）
为什么收它
面向零基础的解释密度高,适合当讲义前置概念的出处。
合法性
自购/个人收藏。来源:主人个人藏书,2026-08 放入收件箱。原始文件不入库,转码文本入库(私有库)。
出版方怎么说
（起草参考,不是我们的判断。真正的「覆盖什么/不覆盖什么」写进 frontmatter 的 claims / notCovered)
它覆盖什么、不覆盖什么
（还没读到能下判断的程度。claims / notCovered 空着就是空着,不猜。)
怎么引用它
(依据: book=prompt-engineering-in-action §1 Introduction to Prompt Engineering)
章节名对不上会被 lab:validate 拦下;页码锚(§p.123)同样可用。
结构(47 段,共 499k 字符)
| 段 | 章节 | 页 | 规模 |
|---|---|---|---|
| 01 | Prompt Engineering in Action | — | 0.2k |
| 02 | welcome | — | 2.6k |
| 03 | Brief contents | — | 0.5k |
| 04 | 1 Introduction to Prompt Engineering | — | 2.3k |
| 05 | 1.1 What is a Prompt? | — | 5.5k |
| 06 | 1.2 Why is Prompt Engineering Important? | — | 19.8k |
| 07 | 1.3 How to use Prompts? | — | 9.2k |
| 08 | 1.4 What is a Large Language Model | — | 1.7k |
| 09 | 1.5 Distinguishing LLMs from Other Generative AI Models | — | 1.5k |
| 10 | 1.6 Insights into the Transformer Architecture | — | 3.1k |
| 11 | 1.7 How does Prompt Engineering vary by LLMs? | — | 13.4k |
| 12 | 1.8 What language and tools are great for Prompt engineering? | — | 8.8k |
| 13 | 1.9 Summary | — | 1.6k |
| 14 | 2 Prompt Patterns: Basic Types and Templates | — | 53.4k |
| 15 | 3 Prompt Patterns: Advanced Types and Templates | — | 1.2k |
| 16 | 3.1 Output Customization | — | 10.0k |
| 17 | 3.2 Prompt Improvements | — | 11.2k |
| 18 | 3.3 Interaction | — | 10.5k |
| 19 | 3.4 Context Control | — | 7.5k |
| 20 | 3.5 Error Identification | — | 15.5k |
| 21 | 3.6 Summary | — | 1.8k |
| 22 | 4 Prompting Techniques I | — | 4.7k |
| 23 | 4.1 Why use Prompting Techniques? | — | 2.2k |
| 24 | 4.2 Prompting Techniques | — | 19.0k |
| 25 | 4.3 Chain of Thought Prompting | — | 12.8k |
| 26 | 4.4 Self-Consistency | — | 11.9k |
| 27 | 4.5 ReAct Prompting | — | 15.9k |
| 28 | 4.6 Prompt Chaining | — | 15.9k |
| 29 | 4.7 Summary | — | 1.5k |
| 30 | 5 Prompting Techniques II | — | 1.8k |
| 31 | 5.1 Open AI’s O3-mini Reasoning Model | — | 3.6k |
| 32 | 5.2 DeepSeek-R1 | — | 4.9k |
| 33 | 5.3 Gemini 2.0 Flash Thinking | — | 3.0k |
| 34 | 5.4 Advanced Chain of Thought Prompting | — | 39.1k |
| 35 | 5.5 Generated Knowledge Prompting | — | 18.9k |
| 36 | 5.6 Graph of Thought Prompting | — | 20.7k |
| 37 | 5.7 Multi-modal Prompting | — | 7.0k |
| 38 | 5.8 Automatic Prompt Engineering | — | 15.4k |
| 39 | 5.9 Summary | — | 2.1k |
| 40 | 6 Retrieval-Augmented Generation | — | 1.4k |
| 41 | 6.1 What is Retrieval-Augmented Generation | — | 1.8k |
| 42 | 6.2 How does a RAG system work? | — | 6.6k |
| 43 | 6.3 Prompt Engineering & RAG | — | 9.8k |
| 44 | 6.4 Architectural Overview | — | 27.7k |
| 45 | 6.5 Stages of a RAG Pipeline | — | 63.1k |
| 46 | 6.6 Summary | — | 2.5k |
| 47 | Appendix A. Setting up LangChain | — | 4.3k |
我们自己的读书笔记(0 篇)
（还没有。读完某章后写进 docs/prompt-engineering-in-action/notes/,那才是这本书对我们的产出。）
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