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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Final output

The final output will look something like this:

"The advantages of using Retrieval Augmented Generation (RAG) include:\n\n1. **Improved Accuracy and Relevance:** RAG enhances the accuracy and relevance of responses generated by large language models (LLMs) by fetching and incorporating specific information from databases or datasets in real time. This ensures outputs are based on both the model's pre-existing knowledge and the most current and relevant data provided.\n\n2. **Customization and Flexibility:** RAG allows for the customization of responses based on domain-specific needs by integrating a company's internal databases into the model's response generation process. This level of customization is invaluable for creating personalized experiences and for applications requiring high specificity and detail.\n\n3. **Expanding Model Knowledge Beyond Training Data:** RAG overcomes the limitations of LLMs, which are bound by the scope of their training...