What to Do When AI Gets Your Product Facts Wrong

If an AI answer gets your pricing, availability or features wrong, first preserve the error and identify its likely evidence. Rewriting your homepage immediately may miss the source that keeps supplying the outdated fact.

Capture a specific error

Save the exact question, answer, date, platform and citations. Write the disputed claim as one sentence and attach the current verified fact. Separate a factual error from an unfavorable opinion or a genuine product limitation.

For example, “This product does not support file export” can be checked. “This product is difficult to use” needs context and cannot simply be marked false because the company disagrees.

Trace the information path

Inspect your official documentation, pricing page, help center and any cited third-party pages. Search for the old wording within material you control. Conflicting pages can make a correction incomplete even when the homepage is accurate.

Worked example: a retired plan limit

Imagine a hypothetical product that raised a storage allowance from 5 to 10 units, but an old help article still lists 5. Update the help article and clarify which plans and effective dates the new allowance covers.

Do not replace the old number everywhere blindly. Historical release notes may accurately describe the earlier state. Add context or a link to current limits where useful, preserving the historical record.

Make the current answer easy to verify

Use a clear product name, exact feature description, relevant conditions and a checked date where it helps readers. Link related pages to the current source of truth. Avoid adding repetitive promotional claims that do not resolve the disputed fact.

You cannot guarantee when a third-party answer system will refresh its information or which source it will use. Report the correction as completed on your site, with external answer behavior tracked separately.

Retest without selecting favorable examples

Use the same prompt wording and comparable settings. Keep repeated observations, including answers that remain wrong. If the error disappears once, describe that as one corrected observation, not a permanent fix.

Prioritize errors by user consequence. Wrong eligibility or pricing information usually deserves attention before minor descriptive wording. The outcome to pursue is a buyer making a decision from accurate product facts.

Find every place the old fact still lives

A wrong answer about your product is usually not a wrong guess. It is an accurate reading of something that was true once and is still published somewhere. Before correcting anything, inventory where the outdated claim still appears, including places your team rarely looks:

• Help center articles and their translations
• Pricing and comparison pages, including archived campaign landing pages
• PDF datasheets, slide decks, and downloadable guides
• App store listings, marketplace entries, and partner directories
• Your own blog posts announcing the earlier state
• Structured data on product pages that was set up once and never revisited
• Third-party reviews and comparison sites that quoted the old figure

Search for the exact old wording and the old number, not just the topic. An assistant can help draft the search list from the disputed claim, but the search itself is done against your real content and the live web. Record each location with its owner and whether you can edit it directly.

Write the correction so it can be quoted

The goal is a current statement of the fact that is easy to find, unambiguous, and self-contained, so that any system or person reading it gets the right answer without needing the surrounding page. A few properties make a statement quotable in that sense:

Put the corrected statement on the page that is the natural source of truth for it, usually a help article or the pricing page, and link other pages to it rather than restating the fact in several places that can drift apart again. If the old value was real at the time, keep the historical release note and add a dated line pointing to the current limit.

Decide what not to fight

Some unfavorable answers are correct, and treating them as errors damages credibility with users and wastes editorial time. Three tests separate a correction task from something else:

1. Is the claim checkable? "No offline mode" can be verified. "Clunky interface" is an opinion; the right response, if any, is design work, not a correction campaign.
2. Was it true recently? If so, the answer system may be reading an outdated source, and the fix is content. If it was never true, look for where the confusion came from: a similarly named product, a misread comparison table, a partner's mistaken listing.
3. Does it matter to a buyer's decision? Wrong pricing, eligibility, or compatibility comes first. A slightly wrong founding year is not worth a workflow.

Track corrections as tickets with the disputed claim, the verified fact, the locations updated, and the retest results across several runs. A single favorable answer after a correction is one observation; a claim that stays wrong after your content is fixed points to a third-party source, which is a different task with a different owner.

Put this into practice

Copy the worksheet columns below into a spreadsheet and keep one row per item you check. The filled row is an illustrative example, not a reported customer result; replace it with your own verified records.

Use the following prompt only after supplying the records it requests:

Compare these recorded AI claims with our verified product facts. Return factual error, supporting official source, conflicting public page, severity and correction action. Separate opinions and real limitations from false statements.

Research context

Experiments with brand information illustrate why an answer's factual claims need independent verification. The related Ahrefs starting points are I Ran an AI Misinformation Experiment. Every Marketer Should See the Results and 6 Ways to Automate AEO With Letaido. This guide’s checklist, examples and proposed workflow are independently written; they are not results of a SEOVision experiment.

Continue with the next task

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Sources

• I Ran an AI Misinformation Experiment. Every Marketer Should See the Results — Research starting point; not an endorsement of this original workflow
• 6 Ways to Automate AEO With Letaido — Research starting point; not an endorsement of this original workflow