When AI engines get your facts wrong: find the source, then fix it

Picture the screenshot that lands in your inbox. An AI answer describes your service with a feature you retired last year. The question that follows is simple: "How do we make it stop?"

You can't command an engine to change an answer. You can find where the wrong fact comes from and fix the sources. That's slower than an email to support, and it works more often than people expect.

Collect the errors systematically

Don't chase one screenshot. Run your brand prompts across models and record every factual error, with the exact wording, model, date and cited sources. Brand prompts include "what is [brand]", "does [brand] offer X", "[brand] pricing" and "[brand] vs [competitor]".

Promptwatch stores each response, its sources and sentiment score, so you can search for errors across weeks. It also generates a weekly AI summary of sentiment movement, which is a prompt to read the lowest-scoring responses first.

Sort errors by type

• Stale facts. Retired products, old prices, old locations, former executives.
• Wrong facts. Claims that were never true.
• Confused identity. Facts about another company.
• Competitor leakage. A rival's feature attributed to you or the reverse.
• Unsupported claims. Statements with no clear source.

Each type has a different path.

Trace each error to a source

Look at what the answer cited. In many cases a specific page contains the wrong fact. It might be your own old page, a third-party review, a directory listing, an old press release or a forum comment.

Use citation analytics to list sources behind the erroneous responses. If no source is shown, the engine may be working from what it learned in training or from an index you can't see. In that case, you can't point to one page, and your fix is to make correct information abundant and consistent.

Fixing your own pages first

Often the culprit is you. Common cases:

• An old blog post stating a price or feature that has since changed.
• A PDF with outdated details that's still indexed.
• Two pages that contradict each other.
• A page that mentions a retired product with no indication that it's retired.

The fix is plain: update or remove the page, or add a clear note about what's changed, and redirect where appropriate. Keep a dated facts page that states current information in plain sentences.

Don't leave old pages up with a vague "archived" label. Say what's different.

Fixing third-party pages

If the error appears on a publisher's page or a directory, contact the owner with a short, polite message and a link to the correct information. Provide evidence. Many will fix a factual slip. Some won't respond, and you can't force them.

For review sites with user-submitted content, follow the platform's process for corrections. Don't create fake accounts to counter negative content, and don't pay for removals that aren't legitimate.

When the source is a community thread

Forum and Reddit threads often hold outdated advice that keeps being repeated. Join the conversation honestly, state who you are and give the current facts. Our guides on how Reddit threads shape AI answers and Reddit and local forums cover participating without being the problem.

Strengthen the correct version

Whatever the source of the error, make the true version easy to find and consistent:

1. Keep your facts page, About page and product pages in agreement.
2. Align structured data with the visible content. See schema markup for AI search.
3. Update your LinkedIn page, directory profiles and other profiles.
4. Publish a short, clear statement of anything that changed, such as a rename or a retired product.

Consistency across sources helps any system that cross-checks.

Be patient and re-measure

After fixes, engines need to re-crawl and refresh. We have no sourced timeline, so we re-run the same brand prompts weekly and watch. In Promptwatch, the Page Tracker shows whether corrected pages are fetched and cited, and the crawler logs show whether bots revisited. If bots haven't returned, the fix hasn't had a chance to work yet.

If the same error appears after a couple of months despite clean sources, escalate to the vendor's feedback channel. Not every engine offers one that works, but it costs little to try.

What not to promise clients

We don't promise that an error will disappear on a date. We promise a process: identify, trace, fix, re-measure, and a record of what we did. That's all anyone can honestly offer.

Put it on the board

Each error is an action with an owner. The Action Board keeps them visible: "update pricing page", "contact directory about old address", "post a clarification in the forum thread". Add comments as things progress.

Our recommendation

Treat wrong answers as a source-tracing problem. Use Promptwatch to read the lowest-sentiment responses, see the cited sources and follow the corrected pages afterwards. Start with the errors on your own domain, since those are fixed in a day.