How Gemini Grounds Answers With Google Search

"Gemini" covers more surfaces than most teams assume. As of October 2026, Google runs four separate generative retrieval paths:

• AI Overviews, the summary at the top of some Google results.
• AI Mode, the conversational search experience inside Google Search.
• The Gemini app, Google's consumer assistant.
• Grounding with Google Search in the Gemini API, a developer tool that is invoked and billed separately.

Google's own AI features documentation says AI Overviews and AI Mode "may use different models and techniques" and can return different links. The Gemini app and the API add two more paths on top. That means one test in one surface tells you nothing reliable about the other three.

The practical consequence: when a tool reports a single "Google AI" result, ask which path it measured. If you see a mismatch, why a tool's 'Google AI' result differs from the AI Overview you see covers the usual cause.

Grounding with Google Search is an optional tool a developer turns on when calling the Gemini API. Google's developer docs describe the flow plainly: the model "automatically generates one or multiple search queries" and executes them, then answers with inline citations to the pages it used.

Three things follow from that design.

1. You never see the real query. The model writes its own searches from the user's prompt. A prompt you test is not the query Google ran.
2. Grounding is per request. If the developer does not enable the tool, the answer comes from the model's training data alone, with no live web access.
3. Billing differs by model. Google bills grounding per search query on Gemini 3 and per prompt on Gemini 2.5 and older, so developers have a cost reason to limit it.

The API returns citations alongside the answer, which makes it a useful measurement surface. If you log those citations over time, the method in logging retrieved vs cited sources via API applies here too. Keep in mind that a citation is a noisy proxy for influence: research on how LLMs cite (arXiv:2606.28358) repaired over 90% of missed citations without changing the sources at all.

The Gemini app and Google's search surfaces share a brand, not a pipeline. As of a September 2026 review of Google's release notes, the Gemini app listed gemini-3.6-flash as a selectable model, while Google had not stated the default for free users. AI Mode had used gemini-3.5-flash as its default since May 2026, according to Google's I/O post. For AI Overviews, Google publishes only "Gemini 3" and nothing finer.

The API is a different product again. Calling the Gemini API with grounding on is a reasonable proxy for the Gemini app. It is not a measurement of AI Overviews or AI Mode, which have no official API.

The controls differ too. For AI Overviews and AI Mode, Google says a page must be "indexed and eligible to be shown in Google Search with a snippet" and that there are "no additional technical requirements." For the Gemini app, the documented publisher control is Google-Extended. For the full comparison, see whether Gemini is the same as Google AI Overviews.

Each Google surface has its own documented switch. None of them makes you more likely to be cited. They decide whether you are eligible or excluded.

A Google-Extended block looks like this:

User-agent: Google-Extended
Disallow: /

Google states Google-Extended "does not impact a site's inclusion in Google Search nor is it used as a ranking signal." So blocking it does not hide you from AI Overviews or AI Mode. Our sources do not say how it applies to developer calls to the Gemini API, so treat that as undocumented.

One gate does not apply to Google: rendering. Googlebot renders JavaScript, unlike most other AI crawlers. Before you change anything, read what blocking Google-Extended does to Gemini's brand answers, because the effect on what the model already knows has never been measured.

Google documents eligibility. It does not document selection. Nothing Google publishes explains which grounded results Gemini chooses to cite, how many it reads, or how it weighs one page against another.

That gap has consequences for anything you read about "Gemini optimization":

• No AI-specific markup. Google's optimization guide says you don't need new machine-readable files, AI text files, markup or Markdown, because Google Search doesn't use them. Ahrefs' matched test found AI Overviews citations fell 4.6% after pages added schema.
• No special writing style. The same guide says you don't need to write in a specific way for generative AI search, and there is no ideal page length.
• No position data. Google's Search Console report for AI features shows impressions for AI Overviews and AI Mode, with no clicks, CTR or position. It does not cover the Gemini app. See whether Search Console reports Gemini app citations.

What holds up is narrow. Research on already-retrieved content (C-SEO Bench and a 252,000-trial factorial) found topical relevance and list position drove citation. Publishers cannot set position. They can state specific facts, such as a price or a one-line definition, on a fetchable page so the engine has a sentence to quote.

Start with access, then look at answers.

1. Confirm your robots.txt. Check whether Google-Extended and Googlebot are allowed, and whether that matches what you intend.
2. Check snippet controls. Look for nosnippet, max-snippet:0 or data-nosnippet inherited from a template.
3. Check the Search Console control. Confirm your property has not opted out of generative AI features, including by inheritance from a parent property.
4. Query each surface separately. Gemini app, AI Overviews and AI Mode can disagree. Record whether each answer was grounded, since an ungrounded answer reflects training data only.
5. Compare API and app. API answers and consumer answers differ in model, defaults and grounding. API answers vs consumer app answers explains the gaps.

For ongoing work, tracking what Gemini says about your brand sets out a repeatable method. For a quick snapshot, the free checker on this site shows whether AI engines name you for up to three buyer questions. Neither tells you why Gemini chose a source, because Google does not publish that.

Does blocking Google-Extended remove me from AI Overviews? No. Google states that Google-Extended does not affect a site's inclusion in Google Search and is not a ranking signal. AI Overviews and AI Mode are part of Search, so they follow Googlebot rules, snippet controls and the Search Console generative AI control. Google-Extended governs training and grounding for Gemini Apps and Vertex, which is a separate decision from Search visibility.

Can I see the queries Gemini ran to find my page? Not from the Gemini app. In the API, the model generates and runs its own search queries when grounding is on, and Google's Search Console AI report shows impressions only for AI Overviews and AI Mode. Bing Webmaster Tools exposes grounding queries for Copilot, but Google has no equivalent as of October 2026.

Does the Gemini API always search the web? No. Grounding with Google Search is a tool the developer must enable. Without it, the model answers from training data with a fixed knowledge cutoff. Because billing applies per search query on Gemini 3, developers may also limit when grounding runs. Any test of Gemini answers should record whether grounding was on, or the results mix two different behaviors.

Will adding schema or an llms.txt help Gemini cite me? Google says no special markup or AI text files are needed for its generative AI features, and that Google Search ignores llms.txt. Ahrefs' matched test found AI Overviews citations fell 4.6% after schema was added. Existing markup does no harm, but it is not a lever for AI answers. Indexing and snippet eligibility are what Google documents.

Do I need server-side rendering for Gemini? Less than for other engines. Googlebot renders JavaScript, so content that appears after scripts run can still reach Google's index. Most other AI crawlers, including OAI-SearchBot, ClaudeBot and PerplexityBot, do not execute JavaScript. If you care about ChatGPT, Claude or Perplexity as well, keep your key facts in the initial HTML.