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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Comparing RAG with model fine-tuning

LLMs can be adapted to your data in two ways:

Fine-tuning: With fine-tuning, you are adjusting the weights and/or biases that define the model’s intelligence based on new training data. This directly impacts the model, permanently changing how it will interact with new inputs.
Input/prompts: This is where you use the model, using the prompt/input to introduce new knowledge that the LLM can act upon.

Why not use fine-tuning in all situations? Once you have introduced the new knowledge, the LLM will always have it! It is also how the model was created – by being trained with data, right? That sounds right in theory, but in practice, fine-tuning has been more reliable in teaching a model specialized tasks (such as teaching a model how to converse in a certain way), and less reliable for factual recall.

The reason is complicated, but in general, a model’s knowledge of facts is like a human’s long-term...