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

Using Prompt Engineering to Improve RAG Efforts

Pop quiz, what do you use to generate content from a large language model (LLM)?

A prompt!

Clearly, the prompt is a key element for any generative AI application, and therefore any retrieval-augmented generation (RAG) application. RAG systems blend the capabilities of information retrieval and generative language models to enhance the quality and relevance of generated text. Prompt engineering, in this context, involves the strategic formulation and refinement of input prompts to improve the retrieval of pertinent information, which subsequently enhances the generation process. Prompts are yet another area within the generative AI world that entire books can be written about. There are numerous strategies that focus on different areas of prompts that can be employed to improve the results of your LLM usage. However, we are going to focus specifically on the strategies that are more specific to RAG applications.

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Table of Contents (20) Chapters

Using Prompt Engineering to Improve RAG Efforts

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Table of Contents (20) Chapters

Using Prompt Engineering to Improve RAG Efforts

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