You're reading from Unlocking Data with Generative AI and RAG Enhance generative AI systems by integrating internal data with large language models using RAG

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781835887905

Length 346 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Keith Bourne

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Table of Contents (20) Chapters

Preface

1. Part 1 – Introduction to Retrieval-Augmented Generation (RAG) FREE CHAPTER

2. Chapter 1: What Is Retrieval-Augmented Generation (RAG)

3. Chapter 2: Code Lab – An Entire RAG Pipeline

4. Chapter 3: Practical Applications of RAG

5. Chapter 4: Components of a RAG System

6. Chapter 5: Managing Security in RAG Applications

7. Part 2 – Components of RAG

8. Chapter 6: Interfacing with RAG and Gradio

9. Chapter 7: The Key Role Vectors and Vector Stores Play in RAG

10. Chapter 8: Similarity Searching with Vectors

11. Chapter 9: Evaluating RAG Quantitatively and with Visualizations

12. Chapter 10: Key RAG Components in LangChain

13. Chapter 11: Using LangChain to Get More from RAG

14. Part 3 – Implementing Advanced RAG

15. Chapter 12: Combining RAG with the Power of AI Agents and LangGraph

16. Chapter 13: Using Prompt Engineering to Improve RAG Efforts

17. Chapter 14: Advanced RAG-Related Techniques for Improving Results

18. Index

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Summary

In this chapter, we explored the crucial role of prompt engineering in enhancing the performance and effectiveness of RAG systems. By strategically designing and refining input prompts, we can improve the retrieval of relevant information and subsequently enhance the quality of generated text. We discussed various prompt design techniques, such as shot design, chain-of-thought prompting, personas, and knowledge augmentation, which can be applied to optimize RAG applications.

Throughout the chapter, we discussed the fundamental concepts of prompt design, including the importance of being concise, specific, and well-defined, as well as the need to iterate gradually and use clear separators. We also highlighted the fact that different LLMs require different prompts, as well as the importance of adapting prompts to the specific model being used.

Through a series of code labs, we learned how to create custom prompt templates using the PromptTemplate class in LangChain, as...