🟢 Introduction

This chapter covers Retrieval-Augmented Generation (RAG) techniques that enhances the capabilities of large language models (LLMs) by integrating external data retrieval.

Instead of relying solely on pre-trained data, RAG allows models to fetch relevant information from external sources, making them more dynamic and adaptable. This architecture solves key limitations of LLMs, such as "hallucinations" (generating false information) and the need for constant fine-tuning to reflect updated data.