Glossary · AI search and AEO
What is grounding in AI search?
Also known as: retrieval grounding, search grounding, retrieval-augmented generation (RAG)
Grounding: Grounding is when an AI model bases its answer on information retrieved at the time of the question, such as live search results or documents, rather than only on what it learned in training. Grounded answers are usually the ones that show source links.
Why does it matter?
Grounding is what makes AI visibility something you can work on this quarter. Training data is fixed until a model is retrained, but grounded answers draw on pages as they exist now, so a new or improved page can become a source.
It also explains why answers differ. The same question can trigger different searches and retrieve different pages, so the sources an engine grounds on can change from run to run.
How do you act on it?
- Make sure pages that matter are crawlable by the search crawlers AI engines use, not just by Googlebot.
- Put the key facts in text on the page (prices, locations, specs, dates), not only in images or PDFs that retrieval may skip.
- Keep time-sensitive pages current with a visible updated date, since grounding is typically used for fresh or factual questions.
Frequently asked questions
Is grounding the same as RAG?
They are closely related. Retrieval-augmented generation (RAG) is the general technique of retrieving documents and feeding them to a model; grounding is the broader idea that the answer is anchored in those retrieved sources. Web-search grounding is one form of RAG.
How do I know whether an answer was grounded?
Source links or citations are the clearest sign. An answer with no sources may still be accurate, but it has most likely come from training data, which you can't update on demand.