Can AI assistants really tell whether my facts are correct?

AI models do not check absolute truth, but they assess signals: do your details agree with other sources, are they internally consistent and backed by evidence? Consistent, well-sourced facts increase your citability and lower the risk of hallucinations about your brand.

Fact-Checking

Q: How often should I check facts on my website?

Critical information such as prices, opening hours or legal values you should check immediately with every change, and otherwise at least quarterly. Static facts such as founding years are fine once a year. What matters is a fixed rhythm so that nothing becomes outdated unnoticed.

Fact-checking is verifying every claim, figure, date and quote on your site against a primary source before it publishes, and re-checking it on a schedule afterward. For AI visibility specifically, it means treating your own pages as the source of truth an assistant could cite word for word, not just as prose that reads well to a human.

Why fact-checking matters for AI visibility

AI assistants summarize and re-state whatever they pull from your pages, often without a human reviewing the output first. The Columbia Journalism Review's Tow Center tested eight AI search tools on 1,600 queries asking them to identify a source article's headline, date and URL: more than 60% of responses were wrong across all eight tools, and ChatGPT alone misidentified 134 of 200 articles. That failure rate exists precisely because these systems generate plausible-sounding answers rather than verified ones. A page with accurate, unambiguous, internally consistent facts gives a model less to get wrong when it paraphrases you, and it gives you a stronger position to point to when it doesn't. Sloppy facts don't just risk one wrong number; they get baked into whatever an assistant tells the next person who asks about your business.

How fact-checking works in practice

Start by listing every checkable claim on a page: prices, dates, figures, names, certifications, quotes. Verify each one against a primary source — your own internal system of record, an official register, a manufacturer's spec sheet — not against a second-hand summary or a competitor's page that might have copied the same error. For any statistic, trace it back to the original study rather than the article citing it. Note where the fact came from and when you checked it, so the verification is auditable later, and set a recheck interval, because facts have a shelf life even when nothing about your writing changes. Keep opinions and forecasts clearly labeled as such — fact-checking applies to claims that can be true or false, not to predictions.

Common mistakes in fact-checking

The most common mistake is checking a claim against another site instead of a primary source — if five pages repeat the same number, that's five copies of one possible error, not five confirmations. Close behind is letting a correct fact go stale: a price or a policy that was accurate at launch and was never revisited. Contradicting yourself across pages is another real cost, since it's an easy inconsistency signal for a model to pick up on when weighing whether to trust you at all. Some sites also add schema markup or an llms.txt file and treat that as a substitute for actually verifying the content — it isn't. Google's own guidance for AI Overviews and AI Mode states plainly that no special markup or AI-specific file is required, and John Mueller has confirmed no Google Search system reads llms.txt at all; an Ahrefs analysis of roughly 137,000 sites that published one found about 97% saw no measurable referral traffic from it. The fix for weak citability is correct, current, sourced content — not a file that promises it.

Relation to E-E-A-T and AI recommendations

Fact-checking is the mechanical work behind the "trustworthiness" component of E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), the framework search engines and AI systems increasingly use to judge content quality. Trustworthiness isn't a claim you make about yourself; it's demonstrated through facts that hold up when checked. Pair fact-checking with visible author profiles, clear update dates and explicit source citations, since those are the signals a model and a skeptical reader both look for when deciding whether your page is worth relying on. None of this guarantees a citation on any given query, but it removes the easiest reason for a model to look elsewhere.

Example

A bicycle shop's guide page states: "E-bikes have a legal top speed of 25 km/h." Before publishing, the editorial team checks that against the actual regulation rather than another blog covering the same topic, confirms that motor assistance on a pedelec does cut out at 25 km/h, links to the regulation, and records the date it was checked. Six months later, when a reader asks an AI assistant about e-bike speed limits, the model has a dated, sourced, unambiguous page to draw on instead of an unverified forum thread making the same claim with no way to confirm it.

Common questions

How often should I check facts on my website?

Check anything that changes on its own — prices, hours, legal limits, availability — the moment it changes, and otherwise on at least a quarterly pass. Slow-moving facts like founding dates or headquarters location are fine on an annual review. The rhythm matters more than the exact interval: pick one and keep it, so nothing goes stale unnoticed.

Can AI assistants actually tell whether my facts are correct?

Not directly — they don't independently verify truth. They weigh signals: does your page agree with other sources, is it internally consistent, does it cite where a claim comes from. Given how often AI tools get source attribution wrong on their own (the Tow Center study above found over 60% error rates), a page that is demonstrably accurate and clearly sourced gives a model much less room to misrepresent you.