A general-purpose AI model knows a lot about the world and nothing about your business. It has never seen your product manuals, your internal policies, or last quarter's reports. Retrieval-augmented generation (RAG) is the technique that closes that gap: it lets a model answer questions using your documents, accurately and with sources, without retraining the model. This guide explains what RAG is, how it works, and when to use it.
TL;DR
RAG retrieves relevant snippets from your own content and includes them in the prompt, so the model answers from your knowledge, not just its training data
It works by converting documents into embeddings, storing them in a vector database, and retrieving the closest matches to each question
RAG reduces hallucination and lets you cite sources, and it is easier and cheaper to keep current than fine-tuning
