Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) is a pattern in which a language model first retrieves relevant passages from a trusted source, such as a document store or search index, and then generates an answer grounded in them. It reduces hallucination and makes answers traceable to sources.

Why it matters at a CIO Cafe table

Terms like Retrieval-augmented generation (RAG) turn up at tables because the disagreement is usually about scope, not definition. Agreeing what the word means is the cheapest way to shorten a decision — which is why every table starts by framing the question in plain language.