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

RAG security challenges

RAG applications face unique security challenges due to their reliance on large language models (LLMs) and external data sources. Let’s start with the black box challenge, highlighting the relative difficulty in understanding how an LLM determines its response.

LLMs as black boxes

When something is in a dark, black box with the lid closed, you cannot see what is going on in there! That is the idea behind the black box when discussing LLMs, meaning there is a lack of transparency and interpretability in how these complex AI models process input and generate output. The most popular LLMs are also some of the largest, meaning they can have more than 100 billion parameters. The intricate interconnections and weights of these parameters make it difficult to understand how the model arrives at a particular output.

While the black box aspects of LLMs do not directly create a security problem, it does make it more difficult to identify solutions to problems...

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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

RAG security challenges

LLMs as black boxes

Authors (1)

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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

RAG security challenges

LLMs as black boxes

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Authors (1)

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