RAG is the method of having a language model read your own documents and ground its answer in them. It's the most practical way to use internal knowledge the model doesn't have.
01. Why It's Needed
A language model doesn't know your contracts, your procedures or your product catalogue. Asked without them, it invents. RAG finds the relevant document before generating and hands it to the model.
02. How It Works
Documents are split into chunks, converted into numerical representations and written into a search layer. When a question arrives, the most relevant chunks are found and sent to the model with the prompt. The model grounds its answer in those chunks.
03. What Changed in 2026
RAG is no longer just vector search plus a model. In enterprise deployments it has become a governed decision infrastructure that influences audit conclusions and risk scores. That has pushed traceability and authorisation requirements to the front.
04. Citing Sources
Which document an answer rests on must be shown to the user. An uncited answer, even when correct, doesn't earn trust in an enterprise setting — and when it's wrong, nobody notices.
05. The Permission Boundary
A document a user cannot see must not enter their answer. The search layer has to filter by user identity. Skip this step and RAG becomes a channel leaking unauthorised information.
06. Where to Start
Begin with one document set and one user group. List the ten most frequent questions and measure whether the system answers them correctly. Expand scope afterwards.