Building a RAG (Retrieval-Augmented Generation) System from Scratch

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of large language models by allowing them to access and utilize external knowledge. This enables them to provide more accurate, up-to-date, and contextually relevant answers. In this hands-on tutorial, we will break down the core components of a RAG system, including document indexing, vector embeddings, and retrieval mechanisms. You will learn how to build a simple but functional RAG system from scratch that can answer questions based on a custom set of documents, giving you a foundational understanding of this critical AI architecture.