LLM Prompt Engineering for Developers: The Art and Science of Unlocking LLMs' True Potential

Table of Contents

1. Preface
2. From NLP to Large Language Models
3. Introduction to Prompt Engineering
4. OpenAI GPT and Prompting: An Introduction
5. Setting Up the Environment
6. Few-Shot Learning and Chain of Thought
7. Chain of Thought (CoT)
8. Zero-Shot CoT Prompting
9. Auto Chain of Thought Prompting (AutoCoT)
10. Self-Consistency
11. Transfer Learning
12. Perplexity as a Metric for Prompt Optimization
13. ReAct: Reason + Act
14. General Knowledge Prompting
15. Introduction to Azure Prompt Flow
16. LangChain: The Prompt Engineer's Guide
17. A Practical Guide to Testing and Scoring Prompts
18. General Guidelines and Best Practices
19. How and Where Prompt Engineering Is Used
20. Anatomy of a Prompt
21. Types of Prompts
22. Prompt Databases, Tools, and Resources
23. Afterword