RAG (Retrieval-Augmented Generation)

RAG is a technique where an AI model retrieves relevant information from your own documents before generating an answer, rather than relying only on what it learned during training.

Most enterprise AI agents run on RAG — existing documents, FAQs, and manuals are enough, with no need to train a model from scratch, and updating knowledge is as simple as swapping documents. A common confusion is treating RAG and fine-tuning as interchangeable: RAG solves 'the model doesn't know your data,' while fine-tuning solves 'the model's behavior needs adjusting' — different layers of the problem.