Retrieval-augmented generation is an AI architecture where the model queries an external knowledge base at inference time and uses the retrieved information to ground its response. It solves the problem of models hallucinating facts they don’t actually know.
RAG is why modern AI answer engines can cite recent, specific information about a business without being retrained on it. Any brand that wants to be cited in AI answers needs to ensure its content is retrievable by these systems.