A mismatch with the official website is not always the same problem. Capture the exact prompt, platform, language, location, time, full answer and cited URLs. Then classify the issue as entity confusion, outdated facts, conflicting evidence, or unsupported inference. This prevents teams from treating every problem as a copywriting task and changing the website without understanding the source chain.
Create an auditable ledger for the facts users are likely to ask about: legal and brand names, domain, location, services, audience, key people, contact details, product status and confusing namesakes. Record the evidence URL, owner, last verification date and disclosure status. First-party pages establish official facts, but self-claims do not replace independent evidence for reputation or market impact.
Inspect visible citations, then search distinctive phrases, old names and incorrect attributes. Fix controlled sources first: stale website pages, structured data, downloadable files and public profiles. For third-party errors, use the publisher's correction process and provide verifiable evidence rather than requesting blanket removal. If the answer shows no sources, compare prompts and languages and label suspected sources as hypotheses—not facts.
First align official facts across core pages, service pages, contact data and structured markup. Second, resolve accessible stale versions and conflicting URLs with appropriate redirects, canonicals or update notes. Third, publish citable explanations, FAQs, definitions and evidence pages. Only then strengthen legitimate third-party corroboration through editorial PR, directories, communities or encyclopedia processes. Fabricated coverage and coordinated fake accounts create risk rather than trust.
Retest the original prompt, close variants, comparison prompts and reverse-check questions. Record the answer, cited domains, wrong field and observation date. One correct response does not prove a permanent fix. A practical closure rule is: core facts are correct, important sources no longer conflict, and the error does not recur across several scheduled checks. Brands can improve the evidence environment; they cannot guarantee when a model will change an answer.
Keep an evidence pack, fact ledger, source-conflict table, action owner list, page-change log, retest matrix, unresolved hypotheses and next review date. This record helps content, technical, PR and legal teams work from the same facts and prevents the next incident from restarting at zero.
Will an AI answer change immediately after we update the website?
Usually not. Crawling, indexing, retrieval and answer generation are separate stages with different timelines. Record the change date and retest a fixed prompt set rather than promising an immediate update.
Can a company ask an AI platform to delete a wrong answer?
Use the platform's feedback, correction or privacy process where applicable. Removal depends on the issue and policy, while factual errors usually still require repair of the underlying public evidence.
Should we create a Wikipedia page to correct AI answers?
Wikipedia is not a brand-controlled correction tool. Eligibility depends on significant coverage in independent reliable sources, and the company does not control the article. Build evidence first.
How should a correction project be evaluated?
Track recurrence of the wrong field, changes in cited sources, consistency between first- and third-party facts, and stability across repeated prompt tests—not a single screenshot.
Source：Google Search Central：创建以用户为中心、可靠的内容