Retrieval & ranking
Grounding
Constraining an AI's answer to retrieved source documents instead of model memory.
Grounding is the practice of constraining a model's answer to retrieved source documents rather than letting it answer from memory alone. A grounded answer is tied to evidence the system actually fetched, which reduces hallucination and lets the engine attribute claims to specific sources.
For visibility, grounding is good news: a grounded engine must cite something, and it prefers sources that clearly support the claim. The clearer and more verifiable your passage, the more likely it is chosen as the grounding for a given statement. An ungrounded answer, by contrast, may name no one, which is why engines answering from pure model knowledge cite far fewer brands.
Making your content easy to ground on means stating specific, checkable claims that map cleanly to the questions buyers ask.
Related terms
Grounding Source
The specific document an AI cites as evidence for a claim in its answer.
Retrieval-Augmented Generation
Fetch relevant documents first, then have an LLM write an answer grounded in them.
Hallucination
A confident AI statement that is factually wrong, unsupported, or invented.
Citation (AI)
When an AI answer attributes or links a specific page as its source.
Fact Density
How many specific, sourced facts a passage packs per sentence.
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