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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E-commerce support

E-commerce is a key area that can benefit significantly from RAG applications. Let’s review a couple of areas where RAG can be applied, starting with product descriptions.

Dynamic online product descriptions

RAG’s ability to generate personalized product descriptions is a game-changer for e-commerce businesses. By leveraging the power of RAG, companies can create highly targeted and persuasive product descriptions that resonate with individual customers, ultimately driving sales and fostering brand loyalty. RAG can produce personalized product descriptions or highlight features that are specifically tailored to the user’s past behavior and preferences, taking into account RAG’s ability to analyze vast amounts of customer data, including browsing history, past purchases, and even social media interactions.

For instance, let’s say Rylee is a user who frequently purchases eco-friendly products. RAG can be used to emphasize...