Grounding

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. The distinction matters because the two failure modes are different. An ungrounded model that is wrong has no way to know it is wrong; a grounded model that is wrong has, at least, a document you can inspect and a chain you can follow back.

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. The retriever has to find the passage, the model has to understand it, and the system has to judge it reliable enough to quote. Each of those steps rewards plain structure over clever phrasing.

Consider a Shopify merchant selling merino base layers. A shopper asks Perplexity which brands make merino that resists odour after several wears. If the product pages bury that property in marketing prose, and the reviews mentioning it sit in a widget the crawler never reads, there is nothing clean to ground against, so the brand goes uncited even though real customers said exactly that. If the same property is stated plainly on the product page and corroborated by readable, indexable review text saying the same thing in customers own words, the brand becomes citable material. The answer engine can ground a sentence about odour resistance in a passage it can actually quote.

This is why grounding sits at the centre of answer-engine visibility. ChatGPT, Perplexity, and Google AI Overviews increasingly retrieve before they answer, and what they retrieve is what gets cited. Optimising for grounding is not about gaming the model; it is about making your information easy to read and easy to verify so it survives the retrieval step.

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 BeyondReviews closes.