grounding
Also called: AI grounding, LLM grounding, source grounding, grounded generation
Grounding is the practice of tying a large language model's output to verifiable external sources (retrieved documents, live web results, or a database) instead of the model's memory alone. Google defines it as connecting model output to verifiable sources of information, which reduces hallucinations.
Grounding sits at the join between retrieval and generation. A grounded system first pulls relevant facts from an outside source, then constrains the model to answer from that material. The model still writes the sentence, but the substance is anchored to something checkable.
Grounding vs RAG
The two terms get used interchangeably, but they are not the same. Retrieval-augmented generation (RAG) is a method: fetch documents, place them in the prompt, generate. Grounding is the principle and the result, the answer is tethered to a source and can point back to it. RAG is one way to achieve grounding. Feeding a live search feed or a maps API are others.
Google’s own implementation is Grounding with Google Search, exposed in the Gemini API through the google_search tool. When it is enabled, the model decides whether a search would help, runs one or more queries, then returns an answer with inline citations plus grounding metadata (the queries it ran and the URLs it used). This is also what supplies the source links in AI Overviews. Grounding is why an AI answer can cover events after its training cutoff and show you where each claim came from, rather than inventing a plausible-sounding fact.
How it affects your traffic
AI answer engines ground their responses on live, crawlable sources, and they cite those sources back to the user. If your pages are what the model grounds on, you earn the citation and the referral click. If they are not, a competitor's page does. Our AI SEO work (/services/ai-seo/) makes your site an easy grounding source: clean structure, verifiable claims, schema markup, and quotable passages that a model can lift and attribute. That is the gap between being the answer in ChatGPT, Perplexity, and AI Overviews, or being invisible there.
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