What is RAG (Retrieval-Augmented Generation)?

A chatbot left to its own devices answers from the memory it was trained on — which contains no trace of your contracts, your prices or your internal procedures. RAG, retrieval-augmented generation, changes the rules of that game: before answering, the system searches your document base — proposals, manuals, regulations, the support ticket history — for the relevant passages and places them in front of the model, which then composes an answer strictly from what it was handed. It is the difference between a new employee answering customers from a vague memory of induction week and one who opens the correct file before speaking. For a company the stake is twofold. Answers become correct and verifiable, with the source cited beside them, and none of it requires the enormous cost of retraining a model. When you update a document, the assistant knows the new version immediately instead of repeating something that stopped being true a year ago — which also shifts the maintenance burden away from machine-learning specialists and onto whoever already owns the documents.