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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What Is Retrieval-Augmented Generation (RAG)

The field of artificial intelligence (AI) is rapidly evolving. At the center of it all is generative AI. At the center of generative AI is retrieval-augmented generation (RAG). RAG is emerging as a significant addition to the generative AI toolkit, harnessing the intelligence and text generation capabilities of large language models (LLMs) and integrating them with a company’s internal data. This offers a method to enhance organizational operations significantly. This book focuses on numerous aspects of RAG, examining its role in augmenting the capabilities of LLMs and leveraging internal corporate data for strategic advantage.

As this book progresses, we will outline the potential of RAG in the enterprise, suggesting how it can make AI applications more responsive and smarter, aligning them with your organizational objectives. RAG is well-positioned to become a key facilitator of customized, efficient, and insightful AI solutions...