Obility Editorial · · 4 min read
A wrong answer is built from evidence you can inspect
A prospect asks an assistant about your company and is told you have no annual plan, that you only serve the US, or that a product you retired two years ago is still your main offer. The first instinct is to look for a way to report the answer and have it corrected.
None of the major engines documents a way for a company to edit what an answer says about it. Google’s help page for AI Overviews offers thumbs up and thumbs down buttons and says the feedback helps improve AI Overviews for everyone, which is a quality signal, not a correction request. What you can change is the evidence an answer is built from. That makes the first job diagnosis, because the right fix depends on where the error came from.
Answers draw on training, live retrieval, or both
An assistant can answer from what its model learned in training, and that knowledge stops at a date. Anthropic’s model documentation lists two dates for each Claude model: a training data cutoff, and a reliable knowledge cutoff, which it defines as the date through which the model’s knowledge is most extensive and reliable. A price change, a rename or a new market launched after that point is missing or thin in this layer.
The other source is live retrieval. OpenAI’s developer documentation for its web search tool says the model can choose to search the web or not based on the prompt, and that when it does, the response includes inline citations for URLs found in its search results. Google describes AI Overviews as a model integrated with its core ranking systems and built to show only information backed up by top web results. The same question can be answered from memory one time and from a fresh search the next, and an error can sit in either layer.
The citation on the wrong sentence tells you where to look
Save the exact prompt, engine, date and full answer, then find the specific sentence that is wrong. If a citation is attached to it, open the cited page. You will usually find one of three things: the page states the wrong fact (an old review, a stale directory listing, a press release, or your own outdated help article); the page is correct and the engine misread it; or the page is about a different company with a similar name.
Google’s own account of early AI Overviews errors, published in May 2024 by Liz Reid, its VP of Search, lists the same causes from the engine’s side: misinterpreting the query, misinterpreting a nuance of language on the web, and not having much good information available. Its best-known example was a satirical article, republished on a software company’s website, that an AI Overview linked for a question almost nobody had asked before. The engine did not invent the claim. It found one of the only pages that addressed the question.
If the wrong sentence carries no citation and keeps appearing without sources across repeated runs, the claim more likely comes from the model’s training. Treat that as a working hypothesis rather than a finding, since interfaces differ in how much of their sourcing they show.
Each cause needs a different fix
A stale third-party page calls for a correction request to its publisher, with the current fact and a page of yours that states it. Your own outdated page needs updating or redirecting, and that includes old help articles, PDFs and pricing pages that are still indexed. A misreading usually means the fact depends on its surroundings, such as a price that only makes sense next to a table or a limit stated three paragraphs before the feature it limits. Rewrite it as one sentence that stays true when lifted out on its own.
Name confusion needs your pages to say plainly who you are: what you sell, where you are based and, where it helps, which similarly named company you are not. A data void, where almost nothing on the web answers the question about you, is filled by whatever does exist, so publish the answer on a page you control. Errors that come from training cannot be patched directly. The practical response is to make the correct fact consistent and easy to retrieve, so that answers which search find the current version instead of relying on memory.
Tracked cases beat one-off fixes
Before acting, separate errors from opinions. “Has no annual plan” is a factual claim you can check against your pricing page. “Expensive for small teams” is framing, and a correction request will not change it; the response there is clearer evidence about who the product suits. Obility’s FactCheck & Sentiment workflow is built on that split: preserve the exact prompt, answer, engine and date, decide whether it is a factual error or an opinion, review your own source pages and any cited third-party material, then keep a record of the correction and check later observations.
Be sceptical of any guide that promises a fix in a set number of days. None of these engines publishes a timeline for how quickly a corrected page changes its answers, and a corrected source improves your odds without guaranteeing an outcome. Re-run the same prompts on a schedule. The evidence you want is the corrected page appearing among the citations, or the wrong sentence disappearing across repeated runs. One clean answer on its own proves very little.