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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19. Other Books You May Enjoy

Prompt design versus engineering approaches

When we discussed the different shot approaches in the Take your shot section, that fell under prompt design. However, we also implemented prompt engineering when we filled in the prompt template with the question and context data we pulled from other parts of the RAG system. When we fill this prompt with data from other parts of the system, you may remember that this is called hydrating, which is a specific prompt engineering approach. Prompt design and prompt engineering have significant overlap and so you will often hear the terms used interchangeably. In our case, we are going to talk about them together, particularly how they can be used to improve our RAG application.

I have seen these concepts described in many different ways over the past few years, and so it would seem that our field still hasn’t formed a complete definition of each or drawn the line between them. For the purpose of understanding these concepts for this...

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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

Table of Contents (20) Chapters

Prompt design versus engineering approaches

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Table of Contents (20) Chapters

Prompt design versus engineering approaches

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