1 Large Language Models and the Need for Retrieval Augmented Generation

 

This chapter covers

  • What is Retrieval Augmented Generation?
  • What are Large Language Models and How are they used?
  • The challenges with Large Language Models and the need for RAG
  • Popular use cases of RAG

In a short time, Large Language Models have found a wide applicability in modern language processing tasks and even paved the way for autonomous AI agents. There are high chances that you’ve heard about, if not personally used, ChatGPT. ChatGPT is powered by a generative AI technique called Large Language Models. Retrieval Augmented Generation, or RAG, plays a pivotal role in the application of Large Language Models by enhancing their memory and recall.

This book aims to demystify the idea and application of Retrieval Augmented Generation. Over the course of this book, you will be presented with the definition, the design, implementation, evaluation, and the evolution of this technique.

To kick things off, in this chapter, we will introduce the concepts behind Retrieval Augmented Generation and investigate its pressing need with the help of some examples. We will also take a brief look at Large Language Models and how one can interact with them. We will, further, discuss the challenges inherent to Large Language Models, how Retrieval Augmented Generation overcomes these challenges and, at the end, list down a few use cases that have been enabled by this technique.

1.1 What is RAG?

 
 
 

1.2 What are Large Language Models?

 
 
 

1.2.1 How do you work with Large Language Models?

 
 
 

1.3 The Curse of the LLMs and the novelty of RAG

 
 

1.3.1 The Discovery of Retrieval Augmented Generation

 

1.3.2 How does RAG help?

 
 
 
 

1.4 Popular RAG use cases

 

1.5 Summary

 
 
 
 
sitemap

Unable to load book!

The book could not be loaded.

(try again in a couple of minutes)

manning.com homepage
test yourself with a liveTest