What Is a Correction Loop?

A correction loop is the operational workflow a brand runs when an AI engine states something false about it: locate the source of the error, fix or displace it, flag it through official channels, and re-test until the answer heals. It is the remediation half of hallucination monitoring — detection without a loop just produces well-documented misinformation.

What are the stages?

1. Diagnose the source. Check the answer's citations. Most brand errors are retrieval errors: the engine faithfully quoted a stale or wrong page. No citation and no matching page suggests a parametric memory error instead.
2. Fix the root page. Update your own page if the source is owned; request a correction if it is a third party (review sites, old press, Wikipedia). Ensure your single source-of-truth page states the correct fact plainly and quotably.
3. Strengthen retrieval competition. Publish or update content that directly answers the triggering prompt, so the correct fact outranks the wrong one at retrieval time.
4. Flag it. Use engine feedback mechanisms — thumbs-down reporting in ChatGPT, answer feedback in Google's AI Overviews and Perplexity — which feed evaluation pipelines even when they do not trigger immediate edits.
5. Re-test on a schedule. Re-run the triggering prompt weekly across engines until the correction holds, then fold it into the standing monitor.

Example

An engine tells users a SaaS product "has no API." The citation points to a 2023 forum thread. The vendor publishes an API overview page, gets the thread's top answer amended, and flags the response. Two crawl cycles later the answer cites the new docs page and the claim flips.

Related terms

See hallucination rate, engine feedback mechanisms, and entity home. Detection cadence for the loop comes from AI mention tracking.