RAG (Retrieval-Augmented Generation)
RAG combines a language model with a retrieval layer so responses are grounded in your own documents and data.
What it is
Retrieval-Augmented Generation first retrieves relevant context from a trusted source, then gives that context to the model before generating an answer.
Why it matters for delivery teams
RAG improves practical accuracy for internal knowledge use cases like runbooks, SOP lookup, architecture notes, or support guidance.
Common mistake
Skipping source quality. If your documents are outdated or inconsistent, RAG will still return weak answers, only faster.
Practical next step
Choose one curated knowledge base and measure answer quality against real team questions before expanding scope.