Grounding
Grounding is the practice of tying an AI-generated answer to verifiable source material, so the model draws from retrieved documents rather than its own memory, which is what lets the answer carry citations back to the pages it actually relied on.
When a model is grounded, it answers from text fetched at query time instead of generating from parameters alone. That retrieval step is what produces the linked citations you see in answer engines: each claim can be traced to a passage, and the passage points to a page. An ungrounded answer has no source to cite, which is why it reads fluently but cannot be checked.
Grounding is also why being a clear, readable source is how you get included. The model grounds against pages it can parse and trust: plainly written, internally consistent, and corroborated elsewhere. A claim stated once on one obscure page is weak grounding material; the same claim stated cleanly and echoed across independent sources is strong. So the practical lever is not gaming the model, it is making your information easy to read and easy to verify.
The honest caveat is that grounding reduces fabrication but does not eliminate it. A model can ground against a source and still misread it, or cite a page that does not really support the sentence, so a citation is evidence of a source, not proof the source agrees. Getting your existing reviews readable, corroborated, and actually citable by search and AI is the gap BetterReviews closes.