Grounding

Grounding is the practice of tying an AI-generated answer to specific, verifiable source material - retrieved documents, structured data, or citations - rather than letting the model rely solely on patterns memorized during training. Grounded answers can point to where a claim came from; ungrounded answers are generated purely from the model’s internal weights and may be outdated or inaccurate.

Grounding happens when a retrieval or tool-use step supplies the model with source text it can quote, summarize, or attribute, and the model is instructed to base its answer on that material rather than free-associating from training data. Search-integrated assistants, RAG pipelines, and browsing modes are all grounding mechanisms; a base model answering purely from memory is not grounded.

Grounding matters for brand visibility because it is the mechanism that turns a web page into a citation. A well-grounded answer engine surfaces the actual source it drew from, giving a brand a name-check and sometimes a link; a poorly grounded one produces confident-sounding claims with no traceable origin, which is exactly where hallucination risk climbs.