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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Prompt parameters

There are numerous parameters that are common among most LLMs, but we are going to discuss a small subset that is most likely to have an impact on your RAG efforts: temperature, top-p, and seed.

Temperature

If you think of your output as a string of tokens, an LLM, in a basic sense, is predicting the next word (or token) based on the data you’ve provided and the previous tokens it has already generated. The next word that the LLM predicts is a product of a probability distribution representing all potential words and their probabilities.

In many cases, the probability of certain words is going to be much higher than most others, but there is still a probabilistic chance that the LLM selects one of the less likely words. Temperature is the setting that dictates how likely it is for the model to choose a word further down the probability distribution. In other words, this allows you to use temperature to set the degree of randomness of the model’...