RAG is a way to make an AI answer from trustworthy sources instead of just its memory. Before the model replies, the system searches a collection of documents, pulls out the most relevant bits, and hands them to the model to work from. This keeps answers current and grounded, and it lets the AI cite where its information came from.
For example, A company help bot uses RAG to pull the exact policy page before answering a customer's refund question.