Gemini Has Your Facts Wrong: How to Correct Them

Every wrong Gemini answer comes from one of two places. A grounded answer draws on pages Gemini retrieved for that question. An ungrounded answer comes from what the model learned in training, frozen at a cutoff.

The quickest test is the answer itself. If Gemini shows sources or links next to the wrong claim, treat the error as grounded. If it shows none, you can't confirm it searched, and the claim most likely comes from training data.

Ask the same question several times, each in a fresh chat. Retrieval is decided per question: Google's Gemini API documentation says the model "automatically generates one or multiple search queries" when grounding is on. One prompt can come back grounded and the next one not. Our comparison of hallucinated versus outdated answers covers the diagnosis in more depth.

A quick sorting guide:

• Wrong fact with a cited source: grounded. Fix the source.
• Old fact, no source: likely training data from before your change.
• Invented fact, no source: the model filled a gap. Publishing the real fact only helps when Gemini searches.

The same split applies to other assistants, as covered in fixing ChatGPT errors.

Open every page Gemini cites next to the wrong claim. One of three things will be true.

1. The cited page is yours and states the wrong fact. Fix it there. Then find every other place the same fact appears on your site. Prices, founding year, headquarters and product names often differ between the pricing page, the about page, old blog posts and the footer. Make them all agree.
2. The cited page belongs to someone else and is wrong. A directory, review site or old press article is now your problem. Ask the owner to correct it. Our playbook on asking third-party sites to fix wrong facts covers the request. When engines cite other sites instead of yours, being named correctly on those sites is the route into the answer. The evidence shows an association, not a guaranteed effect.
3. The cited page is right and Gemini misread it. State the fact as one plain, quotable sentence on a page that can be fetched. A specific fact gives the engine a sentence it can quote, and that is the only on-page change with a positive experimental signal. See whether to publish your prices for the pricing case.

Say you run a project management tool and Gemini tells prospects your entry plan costs $29 when it costs $39, citing a 2024 comparison article. That's a hypothetical, but the lesson holds: the fix is the article, not your homepage.

Check that the corrected page is eligible

Make sure the page carrying the right fact doesn't have noindex, nosnippet, max-snippet:0 or data-nosnippet applied by accident. Google documents these as the controls that keep content out of snippets and its AI answers in Search. Templates sometimes apply them to the wrong block.

Googlebot renders JavaScript, so Google's retrieval is less exposed to client-side rendering than the ChatGPT and Claude crawlers, which don't execute it. Keep load-bearing facts in the raw HTML anyway. Other engines read the same page.

An ungrounded answer reflects training data. Nothing you publish this quarter changes what an existing model already learned. A correction arrives, if it arrives, with a later model trained on newer pages. Google doesn't publish when that happens or what each model saw.

What's in your control:

• Put the correct fact on the open web now. State it on your site and get it right on sites that describe you. Today's grounded answers and future training runs draw on public pages.
• Keep Google-Extended allowed if you want your pages available for Gemini training and grounding. The next section explains why.
• Re-test on a schedule. Models change. As of October 2026, Gemini's release notes list models you can select in the app but don't state which one free users get by default.

What's not in your control: the model's frozen knowledge, and whether it searches for a given question. Nothing Google documents says a correction typed into a Gemini chat updates the answer for other users. Our playbook on correcting facts past a knowledge cutoff covers the longer route.

Google-Extended is the robots.txt token Google documents for Gemini. Per Google's common crawlers page, disallowing it stops your content being used for training and grounding in Gemini Apps and Vertex AI. Google says it "does not impact a site's inclusion in Google Search nor is it used as a ranking signal."

That cuts both ways. Blocking Google-Extended can feel like a way to stop Gemini misquoting you. It does the opposite of what you want. Gemini Apps can no longer ground on your pages, so your correct version drops out. Third-party pages carrying the wrong fact stay eligible.

The block looks like this:

User-agent: Google-Extended
Disallow: /

If you find it in your robots.txt and nobody chose it deliberately, decide whether you want it. A blanket AI block copied from a template is a known way to remove yourself by accident. The robots.txt Checker for AI Crawlers shows how each AI token resolves on your site.

Google-Extended does not control AI Overviews or AI Mode. There is no robots.txt token for those. Googlebot rules and Search eligibility govern them.

Google runs several generative surfaces, and they don't share one answer. Google says AI Overviews and AI Mode "may use different models and techniques" and can return different links. The Gemini app is a separate product. The Gemini API's Grounding with Google Search is yet another retrieval path.

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." If the wrong fact shows up in Search, see wrong facts in AI Overviews.

Test each surface separately. A fix showing in one tells you nothing about the others.

Google documents the scope of Google-Extended and the eligibility rule for AI Overviews and AI Mode. It also documents the snippet controls and states that no special markup is needed. Its AI optimization guide says you don't need AI text files or markup, and don't need to write in a specific way for generative AI search.

Nothing in the sources we've reviewed documents these, as of October 2026:

• A form that lets a brand correct a Gemini answer for everyone.
• How Gemini Apps decides when to search.
• Which retrieved pages Gemini prefers to cite.

For the question of contacting Google directly, see asking OpenAI or Google to correct wrong information.

Skip the fixes that lack evidence. Google says structured data isn't required for generative AI search and that Google Search ignores llms.txt. E-E-A-T isn't a specific ranking factor, so there is no score to raise.

The next move is simple: test, sort each error as grounded or not, then fix the source. The free checker on this site gives a snapshot of how AI engines answer up to three buyer questions about you, which is a quick way to find errors worth sorting.

Will blocking Google-Extended stop Gemini from saying wrong things about my company? No. Blocking Google-Extended stops your pages being used for Gemini Apps training and grounding. It does nothing to the model's existing knowledge or to third-party pages that carry the wrong fact. In practice it can make a grounded error worse, because your correct page drops out of the pages Gemini can draw on while the wrong sources remain available.

How long until Gemini shows my corrected fact? Google publishes no timeline. For a grounded error, the correction can appear once Gemini retrieves the fixed page, but whether it searches for a given question is its decision. For an ungrounded error, the old fact can persist until a later model trained on newer pages replaces the current one, and Google doesn't announce what each model saw.

If I fix my site, will AI Overviews update too? Not necessarily. Google says AI Overviews and AI Mode may use different models and techniques, and the Gemini app is a separate product again. AI Overviews depends on your page being indexed and eligible for a snippet in Google Search. Check each surface on its own after a fix, and confirm the corrected page carries no accidental noindex or nosnippet.

Does adding schema markup help Gemini get my facts right? There's no evidence it does. Google says structured data isn't required for generative AI search. Ahrefs' matched-control study found AI Overviews citations fell 4.6% on pages after schema was added. Existing markup does no harm, so there's no reason to remove it. It just isn't the lever for correcting a Gemini answer. Stating the fact plainly on a fetchable page is.

Can I tell whether a wrong Gemini answer came from training data? Only indirectly. If the answer shows sources, it was grounded on retrieved pages, and you can open them to find the error. If it shows none, you can't confirm Gemini searched, so treat the claim as most likely coming from training data. Asking the same question in several fresh chats helps, because retrieval is decided per question.