AI search glossary
What Is Grounding in AI, and How Do AI Search Tools Use It?
Site owners meet this word in two places: Google’s Google-Extended note and Bing’s AI Performance report. This entry gives the exact meaning in each, and five checks for being a source AI tools can rely on.
What is grounding in AI? The short answer
Grounding in AI means tying a model’s answer to outside sources it retrieves at the moment of answering, such as live search results or a company’s documents. Instead of relying only on what it learned in training, the model writes from that evidence and can link to it, which cuts invented facts.
A quick note on the word. Electricians and aircraft engineers use “grounding” for something unrelated. This entry covers the AI meaning only.
Key takeaways
- Google Cloud’s definition: connecting model output to verifiable sources.
- In Google Search, Google treats grounding and RAG as the same technique.
- Google-Extended controls grounding in Gemini Apps, not in Google Search.
- Bing’s grounding queries show the phrases its AI used to find your cited pages.
- It lowers hallucination risk but does not remove it.
Grounding in AI search: how it works
Grounding in AI search happens between the question and the reply. The system searches an index for evidence, hands the best passages to the model with the question, and asks it to answer from that material. The reply then carries links back to the passages, so a reader can check them.
Google Cloud’s grounding overview puts it in one line. Grounding is “the ability to connect model output to verifiable sources of information”.
It lists three benefits. Fewer hallucinations, answers anchored to chosen data, and auditability through links to sources.
The evidence can come from many places. Google Cloud lists grounding with Google Search, with Google Maps, and with a company’s own documents.
Timing is the key difference from training. Training happens months before a question is asked.
Retrieval happens in the second the question arrives, so a page updated this morning can, in principle, be the evidence for an answer this afternoon.
In public AI search, the evidence is web pages. Google’s generative AI guide says its core ranking systems retrieve them from the Search index.
Grounding vs RAG vs citation: how the three relate
Grounding, RAG and citation describe one chain from three angles. Grounding is the goal: an answer tied to evidence. RAG is a common method for reaching it: retrieve, then generate. A citation is the visible proof: the link a reader sees. You can have grounding without a visible citation, but not the reverse.
| Term | What it is | Who sees it |
|---|---|---|
| Grounding | The aim: tie the answer to sources | Mostly hidden from users |
| Retrieval-augmented generation | The method: fetch passages, then write | Engineers and site owners |
| AI citation | The proof: a link shown with the answer | Everyone reading the answer |
Google blurs the first two on purpose. Its guide calls RAG “a technique (also known as grounding)”.
Our live entry on AI citation covers the last link in the chain.
Grounding in Google products: Gemini, Vertex AI and Google-Extended
Grounding in Google products appears in three forms. The Gemini API offers Grounding with Google Search to developers. Vertex AI offers grounding for businesses, on the web or on their own data. And the Gemini app grounds some replies in Google Search, which is where the Google-Extended control applies.
Google’s common crawlers page gives the most useful definition for site owners. Grounding means “providing content from the Google Search index to the model at prompt time”.
The purpose it states is “to improve factuality and relevancy”.
Google-Extended lets you opt out of that for Gemini Apps and for Grounding with Google Search on Vertex AI. It also covers training of future Gemini models.
It does not touch Google Search. Google says the token has no effect on inclusion or ranking there, so AI Overviews and AI Mode are unaffected.
User-agent: Google-Extended
Disallow: /A developer using the Gemini API gets the result in a structured form: answer text, the queries run, and citation marks linking spans of text to URLs.
Grounding queries: what Bing’s AI Performance report shows
Grounding queries are the key phrases Microsoft’s AI used when retrieving content that it then cited in an answer. They appear in the AI Performance report in Bing Webmaster Tools, launched on 10 February 2026. Microsoft says the list is a sample of overall citation activity, not every query.
Microsoft’s launch post covers Copilot, Bing AI summaries and some partner integrations.
In June 2026, Microsoft defined grounding as “the source material and web evidence the system uses to support and cite its response”.
The same June update added Intents, Topics, Citation Share and Compare. Intents sorts those queries by purpose. Categories include Informational, Commercial, Navigational, Research and Local.
Topics group related queries into themes. Solar panels and residential solar installation, Microsoft’s example, fall under one solar energy cluster.
Grounding for a business: why it matters
Grounding matters for a business because it decides which facts about you reach an AI answer. A grounded answer quotes retrieved pages, so the accuracy of those pages becomes the accuracy of the answer. If the best page available is an outdated directory listing, that is what gets quoted.
It also explains why ranking still matters. The sources come from search indexes, ranked by search systems.
And it gives you data. Bing’s report is the only first-party view of the phrases an AI used to find you.
Digital Magma reads that list against the client’s own Search Console queries. Where the AI’s phrasing differs from the words people type, the page usually needs both forms written in plainly.
Grounding sources: 5 checks before you can be one
Grounding sources have to be reachable, current and easy to quote. Digital Magma runs these five checks on key pages. Each one answers a simple question: if an AI system went looking for evidence on this topic right now, would it find this page and trust what it says?
- Is it indexed? Check Google Search Console and Bing Webmaster Tools. AI answers pull from search indexes.
- Is it allowed? Review robots.txt for Googlebot, Bingbot and Google-Extended, and check firewall rules.
- Is it current? Prices, hours and specs should match reality, with a visible update date.
- Is each claim quotable? One clear fact per sentence, with numbers and names written out.
- Does it agree with your other listings? Conflicting facts across sites give the model reasons to hedge.
Check five catches more problems than people expect. A wrong phone number on one directory can surface in an AI answer.
When Digital Magma finds a conflict like that, we fix the source listing first and the website second. Correcting the evidence is the only lever anyone has over the answer.
Grounding in our client work
Grounding queries go into Digital Magma’s reporting wherever a client has Bing Webmaster Tools set up. They show which phrasing Microsoft’s AI used to reach a page. For an app development company with buyers in six countries, that view is per market, because phrasing differs by country.
The facts check matters most for local businesses. For a Toronto ENT clinic, consistent clinic details across the site and profiles are the ground truth an AI answer should draw on.
For a Canadian welding equipment manufacturer visible in ChatGPT and AI Overviews, the indexed specification pages are the evidence those answers rest on.
Grounding in AI: frequently asked questions
Digital Magma’s short answers to the questions SEO practitioners ask us most.
What does grounding mean in AI?
It means connecting a model’s answer to outside sources retrieved at answer time, so the reply rests on evidence rather than memory alone, and can link to it.
What is grounding in generative AI?
In generative AI, grounding gives the model relevant documents or search results with the prompt. The model writes from them, which improves accuracy and allows citations.
Is grounding the same as RAG?
Google treats them as the same in Search: RAG is “also known as grounding”. More broadly, grounding is the aim and RAG is the most common way to achieve it.
What is Grounding with Google Search?
It is a Google tool for developers and Vertex AI users that lets Gemini run Google searches and answer from the results, with citations linking text to source URLs.
What are grounding queries in Bing Webmaster Tools?
They are the key phrases Microsoft’s AI used to retrieve content it cited. They appear in the AI Performance report as a sample, not a complete list.
Does blocking Google-Extended stop grounding?
It stops your content being used for answers in Gemini Apps and for the Google Search tool on Vertex AI. It has no effect on Google Search or AI Overviews.
Does grounding stop AI hallucinations completely?
It does not. Google Cloud says grounding reduces the chances of invented content. A model can still misread a source or rely on a poor one.
Grounding-ready pages AI tools can trust
Digital Magma checks indexing, crawler access and fact consistency, then reads your Bing query data to see how AI finds you. See our GEO agency service or book a free SEO audit.
No AI citation is guaranteed. Call +91 79823 19697, email contact@digitalmagma.com or message on WhatsApp (Mon–Sat, 9 am–9 pm IST). You can read our Google reviews before you get in touch.