What is RAG and how does it affect AI visibility?

RAG — retrieval-augmented generation — is the technique behind most modern AI answers about real businesses: instead of relying only on what a model memorized during training, the assistant retrieves relevant, current web pages at answer time and generates its response grounded in those sources. This matters enormously for AI visibility because it means your recommendation often hinges not on being baked into the model, but on being one of the pages the system retrieves and trusts in the moment a buyer asks. To win in a RAG world you need three things: to be reachable and readable by the retrieval crawlers, to be a clear, well-structured source the system can confidently extract an answer from, and to be corroborated across enough trusted places that you clear the credibility bar for inclusion. RAG is why fresh, structured, widely-cited content beats a static reputation — the assistant is effectively running a live search and quoting the best sources, so becoming one of those sources is the whole game.

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