What is grounding in AI search?
Grounding is the step where an AI system fetches sources at the moment a question is asked and writes its answer from them, instead of relying only on what the model memorized in training. Google defines it as "providing content from the Google Search index to the model at prompt time to improve factuality and relevancy" [1].
Ask Gemini "which airlines fly direct from Denver to Lisbon this winter" and the model cannot answer reliably from training data, which is months or years old. With grounding switched on, the system runs searches, reads the pages that come back, and writes an answer from those pages. The links shown beside the answer are the pages it was grounded on.
For anyone working on AI visibility, grounding is the step that matters most. A page cannot be cited by a grounded answer unless it was retrieved during grounding.
How does grounding work?
Grounding works in four steps: the model decides whether it needs outside information, generates search queries, reads the results, and writes an answer linked back to the sources it used. Google's Gemini API documentation describes that sequence for Grounding with Google Search [3]:
- Prompt analysis. "The model analyzes the prompt and determines if a Google Search can improve the answer" [3]. Not every prompt is grounded.
- Search. If it decides to search, "the model automatically generates one or multiple search queries and executes them" [3].
- Processing. The model "processes the search results, synthesizes the information, and formulates a response" [3].
- Attribution. The response comes back with grounding metadata: the queries the model ran (
webSearchQueries), the web sources it drew on (groundingChunks), and links between specific sentences of the answer and the sources that support them (groundingSupports), which is what makes inline citations possible [3].
Step 4 is the detail most explanations miss. Grounding attaches sources to individual sentences, not to the answer as a whole. A page is cited because a specific passage on it supported a specific claim.
How does grounding work in Google AI Overviews and AI Mode?
In Google Search, grounding uses Google's ordinary ranking systems to pick the pages an AI Overview or AI Mode answer is built from. Google's May 2026 guide says retrieval-augmented generation, "also known as grounding", relies on Search ranking systems to retrieve relevant, up-to-date pages, then uses information from those pages to write the response while showing links to pages that support it [2].
Two consequences follow:
- Ranking comes first. Google described AI Overviews in 2024 as integrated with its "core web ranking systems" and designed to carry out traditional search tasks [6]. A page that does not rank for any of the searches behind an answer has little chance of being grounded on.
- Grounding searches go beyond the typed query. Google says AI Overviews and AI Mode may use query fan-out, issuing related searches across subtopics [2]. A page can be retrieved for a fan-out search the user never typed.
Ranking well does not guarantee the citation, though. In Search Insider's study of 17,240 ranking pages, the AI Overview regularly passed over a top three result and quoted a lower page, such as a Reddit thread. Grounding retrieves candidates through ranking, then the model picks the passages it actually uses.
Why does grounding matter for getting cited?
Grounding matters because it is the only route by which a live web page becomes a citation in an AI answer. A model's training data shapes what it "knows", but training does not produce links. Citations come from the pages retrieved at answer time, which Google surfaces as "prominent, clickable links" [2].
That gives a practical split:
| Training | Grounding | |
|---|---|---|
| When it happens | Before the model is released | Each time a prompt is answered |
| What it uses | A snapshot of text | Live search results |
| Produces a citation link? | No | Yes |
| Can change this week? | No | Yes, as rankings and pages change |
| Google's control | Google-Extended [1] | Google-Extended for Gemini Apps [1]; Search controls for AI Overviews |
Brand mentions absorbed in training can help a model describe a brand, which matters for brand hallucination. But only grounding can put your URL in front of a reader.
What is the difference between grounding and RAG?
Grounding is the goal (an answer anchored to real sources) and retrieval-augmented generation (RAG) is the most common technique for reaching it, so in AI search the two terms are used almost interchangeably. Google's own guide treats them as the same thing, describing RAG as a technique "also known as grounding" [2].
The distinction is still useful. RAG names an architecture, introduced in a 2020 paper by Patrick Lewis and colleagues, that pairs a language model with a retrievable index so the system can provide "provenance for their decisions" [5]. Grounding is the broader idea. Google Cloud offers grounding on Google Search, Google Maps, private document stores and other sources, describing the aim as connecting "model output to verifiable data sources" to reduce hallucinations [4]. RAG over a company's own documents is grounding too; it simply grounds on something other than the web.
When did grounding become part of AI search?
Grounding in AI search grew out of retrieval research, with the RAG paper in May 2020 giving the method its common name [5]. It became a public, named feature once chat assistants were connected to live search, and Google now documents it as the basis of its AI features in Search [2].
2020: retrieval-augmented generation is named
The RAG paper, submitted on May 22, 2020, combined a model's built-in "parametric" memory with a searchable Wikipedia index and reported more specific and factual output than a model without retrieval [5].
2023: grounding becomes a publisher control
When Google launched Google-Extended on September 28, 2023, the announcement spoke of helping improve Bard and Vertex AI models. Google's current documentation states that Google-Extended also governs grounding in Gemini Apps and Grounding with Google Search on Vertex AI [1], making grounding something a site owner can switch off for those products.
2024: AI Overviews and the limits of grounding
In May 2024, after AI Overviews surfaced a satirical answer about eating rocks and forum advice about glue on pizza, Google explained that AI Overviews "generally don't 'hallucinate'" but can misread queries or ground on poor sources where little good information exists [6]. That episode showed the core weakness of grounding: the answer is only as good as the pages retrieved.
2026: grounding queries become visible
Bing Webmaster Tools launched an AI Performance report in public preview on February 10, 2026, reporting citations and "grounding queries", the searches an AI system ran before citing a page [7]. It arrived about four months before Google's own AI report in Search Console [7].
Can grounding still produce wrong answers?
Yes. Grounding reduces hallucination but does not remove it, because a grounded answer inherits every error in the pages it retrieves and can still misread them. Google's own account of early AI Overviews errors blamed misinterpreted queries, misread language on the web, and topics where "not having a lot of great information available" let weak sources through [6].
There is a second limit. Grounding metadata shows which sources supported which sentences [3], but no AI search product publishes why one retrieved page was used and another ignored. Claims about exact grounding "ranking factors" are guesses unless they come from a controlled test.
How do you get your pages used for grounding?
To get your pages used for grounding, make them rank for the searches an AI system will run and make each key passage easy to quote on its own. Google's guide says there are no special files or markup required for its AI features [2], so the work is ordinary SEO aimed at a passage-level reader:
- Rank for the fan-out, not just the head term. Cover the subtopics and follow-up questions an AI system is likely to search for.
- Answer in the first sentence of each section. Grounding links sentences to sources [3], so a self-contained answer is easier to attach to a claim.
- Be the source of record. Original figures, definitions and dated facts give a model something it can only attribute to you.
- Keep Google-Extended open if Gemini citations matter. Blocking it opts the site out of grounding in Gemini Apps [1].
- Watch grounding data where it exists. Bing's AI Performance report shows grounding queries and cited pages [7]; Google's Search Console AI report shows impressions but not the queries behind them [7].