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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

By : Keith Bourne
5 (2)
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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

5 (2)
By: Keith Bourne

Overview of this book

Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.
Table of Contents (20 chapters)
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1
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
7
Part 2 – Components of RAG
14
Part 3 – Implementing Advanced RAG

Code lab 5.3 – Blue team defend!

This code can be found in the CHAPTER5-3_SECURING_YOUR_KEYS.ipynb file in the CHAPTER5 directory of the GitHub repository.

There are a number of solutions we can implement to prevent this attack from revealing our prompt. We are going to address this with a second LLM that acts as the guardian of the response. Using a second LLM to check the original response or to format and understand the input is a common solution for many RAG-related applications. We will show how to use it to better secure the code.

It is important to note up front, though, that this is just one example of a solution. The great security battle against potential adversaries is always shifting and changing. You must continuously stay diligent and come up with new and better solutions to prevent security breaches.

Add this line to your imports:

from langchain_core.prompts import PromptTemplate

This imports the PromptTemplate class from the langchain_core.prompts...

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