GLOSSARY
Grounding
- Grounding
- Grounding is how an AI engine ties its answer to retrieved sources instead of generating purely from memory — fetching current pages and constraining the response to what they say, with citations pointing back. Grounded answers are where citations come from, and being retrievable and quotable is how you get into them.
A language model generating from memory alone produces fluent text with no accountability — its training data is months old and it may blur or invent details. Grounding fixes this by anchoring generation to retrieved documents: the engine searches, reads current pages, and composes an answer constrained to what those pages support, citing them as it goes. This is RAG viewed from the output side: retrieval is the plumbing, grounding is the discipline the answer obeys.
For visibility, grounding is the mechanism that makes your content matter at answer time. A grounded answer about your market is assembled from the pages the engine could retrieve and considered trustworthy enough to lean on. If your pricing page is blocked to AI crawlers or written without a quotable passage, the engine grounds on a competitor's page or a third-party summary — and the answer reflects their version of the facts, including their version of facts about you.
Engines differ in how strictly they ground: Perplexity ties claims to sources densely, while chat-first products mix retrieval with model memory more freely — one reason per-engine measurement matters. Example: ask about a specific advisor's fee structure, and a well-grounded answer quotes the advisor's own fee page; a poorly grounded one paraphrases a stale directory listing. Publishing clear, current, retrievable facts is how you improve which of those happens. When grounding fails or gets skipped, you get hallucination.