What is grounding in AI search?
Grounding is tying an AI model's answer to outside sources, such as live search results, so its claims rest on retrieved content rather than on memory alone. A grounded answer can point to where each claim came from; an ungrounded one is the model speaking from memory. For a brand, grounding decides whether your current pages, or someone else's, are what the answer rests on.
How does grounding work?
Grounding works by giving the model retrieved content at the moment it answers and tying parts of the answer back to it. Google describes Grounding with Google Search as connecting the Gemini model to real-time web content; a grounded response comes back with the search queries the model ran and citation annotations that link spans of the answer to source URLs (Gemini API docs, updated October 13, 2026).
Anthropic's Citations feature returns, for each claim, the exact cited text and its location in the source document, and because the API extracts that text itself, Anthropic says citations are guaranteed to contain valid pointers to the provided documents (Claude docs, read Oct 15, 2026).
Which engines ground their answers, and in what?
Each engine grounds in its own search source:
- Google AI Overviews and AI Mode: Google's search index, with supporting links from indexed, snippet-eligible pages.
- Microsoft Copilot: queries it writes and sends to the Bing search service, per Microsoft's documentation.
- ChatGPT: web searches it chooses to run, with inline citations for URLs from the results.
- Claude and Perplexity: their own web search, with sources listed beside the answer.
What does grounding look like in a real example?
In the public Tesla example (Oct 4, 2026), Claude answered "What is Tesla?" and "Is Tesla legit?" from what the model already knew, with no sources cited: ungrounded answers. On 25 buyer questions it grounded heavily, with 351 citations, its most cited site en.wikipedia.org at 16 and tesla.com at 1.
Google AI Overviews grounded every answer in Google's index and opened "What is Tesla?" with the magnetic unit of the same name. Grounding makes an answer checkable; it does not make it about the right Tesla.
Why does grounding matter for brands?
Because a grounded answer is the part you can influence this month. It reads pages that exist now, so a corrected price or a new comparison page can reach it once it is crawled; an ungrounded answer changes only with a newer model.
It is also where your content controls apply. Google-Extended lets a site say whether Google may use its content for grounding in Gemini Apps and Grounding with Google Search on Vertex AI, and Google states it does not affect Google Search (Google's crawler docs, updated July 14, 2026).
How does Proofsource show grounding?
Proofsource keeps every answer with the pages it cited and, where the engine reports them, the searches it ran, so you can tell a grounded answer from a memory answer and see which sources it rested on. See Citations.
Common questions about Grounding
Does grounding stop hallucinations?
It reduces them by giving the model sources, but a grounded answer can still misread a source, pick the wrong one or blend two. Check the cited page, not just the presence of a citation.
Is grounding the same as RAG?
Closely related. RAG is the retrieve-then-write method; grounding is tying the answer's claims to what was retrieved. Most AI search engines do both.
Can I tell if an answer was grounded?
Usually by its sources: a grounded answer lists or links them. An answer with no sources at all was most likely written from the model's memory.
Does blocking Google-Extended remove me from AI Overviews?
No. Google-Extended covers Gemini training and grounding in Gemini Apps and Vertex AI. Google says it does not affect inclusion or ranking in Google Search, which includes AI Overviews.
Related terms
- Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is a method where a language model first retrieves relevant documents and then writes its answer with them in view.
- Training data vs live search
Training data is what a model learned before its knowledge cutoff; live search is what it looks up on the web while it answers, and one AI answer can draw on either or both.
- AI hallucination
A hallucination is a confident statement in an AI answer that is false or unsupported, such as a wrong price, a feature that does not exist or a source that was never written.
- Google-Extended
Google-Extended is a robots.txt token that controls whether content Google crawls may train future Gemini models and ground Gemini answers, without any effect on Google Search.
Worked example: Tesla, public AI answers, Oct 4, 2026: Proofsource's first scan of Tesla, 25 buyer questions and 5 brand questions, one answer per question on each of four engines. A small sample from one brand, shown as an example, not a benchmark.
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