The white paper adds a 'wrong-description correction SOP'. A wrong statement repeatedly cited by AI hardens like cement, and correction cost rises exponentially with time. This article gives the five-step correction method, three typical errors and the bottom line: do not delete, do not fight, do not report.
The previous chapters are about 'how to make AI say it right'. But there is a more urgent and more easily delayed scenario: AI is already saying it wrong - what then? The 2027 edition lists this as a standalone SOP because it is too easily overlooked.
I. Why correction is more urgent than 'making AI say right'
In the baseline audit, 'being misunderstood' is listed as a state. It is most urgent because a wrong statement repeatedly cited by AI hardens like cement - fixing it in week one versus year two are two completely different things. This is not rhetoric: AI answer gets re-cited by other content, and the wrong information enters the next round of corpus.
II. The five-step correction SOP
- 01 Evidence: screenshot the engine and prompt, the full answer and its cited sources, building a reviewable evidence chain;
- 02 Attribute: judge which layer is wrong - official website (missing entity info), third party (stale or false info), or stale content;
- 03 Fix the source: publish a verifiable fact on the official website, with source and date, as the authoritative correction source;
- 04 Build alternative sources: make 2-3 platforms show the consistent correct statement, forming multi-source corroboration;
- 05 Review: after 4-8 weeks, re-check with the same prompt on the same engine, confirming the wrong statement has been diluted.
III. Three typical errors and how to handle them
| Error type | Typical symptom | Correction focus |
|---|---|---|
| Wrong business description | AI says you do business A, but you do B; or confuses you with a same-name company | Deploy Organization / About structured pages on the official website, clarifying business scope and entity info |
| Wrong parameters / prices | The model, spec or price AI reports is from three years ago | Publish an authoritative parameter page with dates on the official website, and push 2-3 citing sources to update in sync |
| Negative reviews amplified | AI proactively flags your negative info on recommendation questions | Fix the real problem first, then accumulate verifiable positive sources; do not delete posts, do not buy reviews |
IV. The most important rule: do not delete, do not fight, do not report
The only effective method is to dilute it with more real, verifiable, tone-consistent positive sources - let the quantity and authority of the correct statement outweigh the wrong one, and AI naturally changes its tune. Deleting posts is usually ineffective and easily backfires. Anti-patterns: batch reporting, buying reviews, attacking sources - once identified, the penalty is not a single piece of content but the trust weight of the entire domain.
V. The hardest step is admitting the problem
If AI negative description is true, then it is not 'wrong information' but real feedback. In that case any correction is ineffective - the correct move is to fix the product, the service, the process first, then talk about sources. Using sources to cover up real problems is reverse GEO, a compliance red line. The bottom line of white-hat GEO is always one line: make correct information easier for AI to cite, avoid letting wrong information be systematically amplified.
VI. Make correction a routine action
- Include 'being misunderstood' in the monthly baseline audit, not wait for it to ferment;
- On discovering an error, immediately run the five-step SOP - earlier is cheaper;
- Completing correction is not the end; re-check and log after 4-8 weeks;
- Hand the real problem to the product and service owners; sources are only the last bridge.
Closing
The core of the correction SOP is not 'delete' but 'dilute' and 'correct'. In the AI era, a brand's reputation is no longer decided only by what it says, but by how many sources correctly repeat the verifiable fact.