Retrieval-augmented generation (RAG) is a technique where an AI model first retrieves relevant documents, such as web pages, and then writes its answer using them.
It is how AI search features stay current: instead of relying only on what the model learned in training, the engine looks things up at the moment of the question. For brands, it means changes to a page can show up in answers once the page is recrawled, without waiting for a new model. See how AI engines choose which products to recommend.