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

Code lab 13.1 – Custom prompt template

The Prompt template is a class representing the mechanism to manage and use prompts in LangChain. As with most templates, there is text provided, as well as variables that represent inputs to the template. Using the PromptTemplate package to manage your prompts ensures that it works well within the LangChain ecosystem. This code builds off the code we completed in Chapter 8’s 8.3 code lab, and can be found in the CHAPTER13 directory of the GitHub repo as CHAPTER13-1_PROMPT_TEMPLATES.ipynb.

As a refresher, this is the template we have used the most:

prompt = hub.pull("jclemens24/rag-prompt")

Printing this prompt out looks like this:

You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know.
Question: {question}
Context: {context}
Answer:

This is stored in the PromptTemplate object...

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Table of Contents (20) Chapters

Code lab 13.1 – Custom prompt template

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Table of Contents (20) Chapters

Code lab 13.1 – Custom prompt template

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