In Plain Terms
Retrieval-Augmented Generation is a pattern where, before an AI model answers a question, the system first retrieves relevant chunks of real data — internal documents, a knowledge base, product catalog entries — and includes them in the prompt, so the model answers from that specific, current information instead of guessing from its training data.
Why It Matters
A general-purpose model doesn't know a company's internal policies, current product catalog, or last week's support tickets. RAG is what lets an AI support assistant or internal tool give accurate, specific answers about that business's actual data, and — critically — reduces the model inventing plausible-sounding but wrong answers on topics it has no real information about.
What the Engineering Actually Involves
The real work is in the retrieval half, not the AI half: chunking documents sensibly, building a search index (often vector-based) that actually returns the relevant chunk for a given question, and keeping that index in sync as the underlying data changes. A RAG setup is only as good as its retrieval — a good model on top of bad retrieval still gives wrong answers.