Grounding

Grounding is connecting an AI model's output to verifiable sources of information. Google Cloud's documentation defines it as "the ability to connect model output to verifiable sources of information," and lists three benefits: fewer hallucinations, answers anchored to your data sources, and auditability through links to sources. A model can be grounded in web search results, in a company's own documents, or both.

Why it matters for AI visibility

When an AI engine grounds an answer in web results, the pages it retrieves shape what it says about your brand, and some of them appear as citations under the answer. Grounding is also something site owners can control. The Google-Extended token lets a site opt out of grounding in Gemini Apps and in Grounding with Google Search on Vertex AI without affecting Google Search, according to Google's crawler documentation.

How it's measured

Bing Webmaster Tools reports grounding directly. Its AI Performance report counts how often your pages are cited in Microsoft Copilot and in Bing's AI-generated summaries, and lists grounding queries: "the key phrases the AI used when retrieving content that was referenced in AI-generated answers." Those phrases show which questions your pages answer for the AI.

For other engines, read the citations in each answer. If an engine cites a third-party roundup for "best expense software for startups", it grounded that answer in the roundup, so the roundup is where to work: send its author current facts, or earn a place in the next update.

A grounded answer can still be wrong when its source is wrong or out of date, and the fix is to get that source corrected. A hallucination is a different problem: the model states something no source supports.