What is RAG? A Beginner's Guide to Retrieval-Augmented Generation (With a Full Pipeline Walkthrough)
RAG (Retrieval-Augmented Generation) is an AI framework that integrates information retrieval into Large Language Models (LLMs) to improve factuality and relevance. It addresses challenges of frozen LLMs, high retraining costs, and regulatory requirements. RAG decouples knowledge from the model, allowing for dynamic retrieval and up-to-date answers. To implement RAG, a knowledge base is prepared by gathering relevant documents, and then the LLM looks up information in the base to answer questions. This approach is particularly useful for regulated industries where answers need to be backed by sources.
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