1 LLMs and the need for RAG
This chapter covers
- The limits of LLMs and the need for RAG
- The RAG basics
- Popular use cases of RAG
In a short time, large language models (LLMs) have found widespread application in modern language processing tasks and autonomous AI agents. OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, and Meta’s Llama series are notable LLMs integrated into various platforms and techniques. Retrieval-augmented generation, or RAG, plays a pivotal role in the LLM application by enhancing the accuracy and relevance of responses. According to Grand View Research (https://mng.bz/BzKg), in 2023, the global RAG market was estimated at some $1 billion USD, and it has been projected to grow by 44.7% annually, which makes it one of the fastest-growing AI methodologies.
This book aims to demystify the idea of RAG and its application. Chapter by chapter, the book will present the RAG definition, design, implementation, evaluation, and evolution. To kick things off, this chapter begins by highlighting the limitations of LLMs and the need for an approach such as RAG. It then introduces the concept of RAG and builds toward a definition. The chapter ends by listing the popular use cases enabled by RAG.
By the end of this chapter, you will gain foundational knowledge to be ready for a deeper exploration of the RAG system components. In addition, you should