When AI Gets It Wrong About You: A GEO Fact-Correction Playbook

There is a situation worse than "not cited": cited, but cited wrongly.

The most dangerous AI search output is not a refusal to answer. It is a confident wrong conclusion with a source pointing at you. The user sees a name they recognize and believes it.

If you do GEO long enough, this happens. I have run several rounds of it and have a process that mostly works.

1. First, understand why AI gets it wrong

AI errors are not simply "misremembering." There are five distinct sources, and each needs a different fix.

1.1 Your content is genuinely ambiguous

You wrote "some devices need unknown sources enabled to install." AI extracts "installing APKs requires enabling unknown sources."

This is not an AI error. Your sentence permitted that extraction. The qualifier "some devices" was dropped during extraction.

1.2 Conflicting sources, and AI picked the old one

The same fact: your article from three years ago says A, last year's says B. AI crawled both and chose A — likely the more confident-sounding or higher-weighted one.

1.3 Summarization flipped the meaning

When a long paragraph gets compressed into one sentence, causality gets truncated. "If you don't clear the cache, installation may fail" compressed into "clearing the cache can fail installation" is absurd but not rare in AI summaries — nested conditionals are especially vulnerable.

1.4 Training data cutoff

Model knowledge has an expiration date. Product changes, policy updates, and UI redesigns from this year may be entirely unknown; the model answers as if it were the old version.

1.5 You weren't actually cited

The AI invented a claim that sounds like you and attached your domain as backing. Confirm the citation exists before doing anything.

2. Before correcting: verify, don't rush to edit

This step is critical and most people skip it, then fix the wrong thing.

AI output is probabilistic. Ask the same question five times and you may get three wrong and two right. If you start rewriting content after seeing one bad answer, you are reacting to noise.

Verification loop:

1. Reproduce three or more times. Same query, varied phrasing. Is the error stable?
2. Pull the source. Put the AI's wrong conclusion next to the exact paragraph in your page, line by line.
3. Classify the cause. Map it to the five sources above.
4. Decide whether it warrants a fix. Occasional (one in three) usually doesn't. Stable (three of three) definitely does.

Only after step 4 says "stable" do you edit.

3. Correction paths by cause

Case A: ambiguous source text → restructure the sentence

Easiest to handle and highest payoff.

Bad:

Some older Android versions require enabling unknown sources to install third-party apps, while newer versions authorize per app.

What AI extracted (observed): "installing APKs requires enabling unknown sources"

Fix: three-part definition block

Conclusion: Android 8.0 and above authorizes per app; global unknown sources is no longer required. Distinction: Android 7.1 and below requires global enabling. Reason: 8.0 introduced the per-app "install unknown apps" permission model.

Put the conclusion in its own paragraph, bolded, first. Express qualifiers as independent sentences, not subordinate clauses. AI extraction favors standalone short sentences and paragraph-initial statements, which carries the full scope along.

Case B: conflicting sources → unify and mark deprecation

Do:

1. Find every older article containing the fact
2. Add a prominent update banner at the top: > ⚠️ Written in 2024. This conclusion has been superseded — see "XXX"
3. In the new article, state explicitly: "It was previously claimed that... In practice this has changed to..."
4. Do not delete old articles — their links and authority remain, and deletion creates fresh 404s

Key: create an explicit "timestamp + pointer" relationship. AI then reads "this one is old, look at that one" instead of treating them as equal competing sources.

Case C: compression reversal → dismantle nested conditionals

Rule: one fact, one sentence, one paragraph.

Don't write:

If the app has not been granted storage permission, and the system version is below 11, then in the absence of scoped storage the downloaded APK is stored in the external storage root, otherwise in the app-private directory.

Write three blocks:

Below Android 11: downloaded APKs go in the external storage root. Android 11+ without scoped storage: still external root, but requires MANAGE_EXTERNAL_STORAGE. Android 11+ with scoped storage: stored in the app-private directory.

Nested conditionals are the epicenter of AI summary errors. Split them so every sentence stands alone, and extraction cannot fail.

Case D: training cutoff → publish freshness signals

You cannot fix this by editing content; the model cannot see your update. What you can do:

1. Display a last-updated date prominently (not the publish date)
2. Communicate it via dateModified structured data
3. Keep content current long term so the next retrain or live retrieval picks it up

Retrieval-augmented engines (Perplexity, ChatGPT with browsing) reflect your update faster. Pure training-based answers must wait. Set expectations accordingly.

Case E: fabricated citation → log it, don't over-invest

If AI attributes words to you that you never wrote while linking your domain, that is hallucination. You have limited leverage:

• Keep screenshot evidence
• If it is widespread and persistent, use the engine's feedback channel
• Make the correct statement clear and repeated on your site to raise selection probability

Do not do large-scale content surgery to fight hallucination. The ROI is terrible.

4. Verifying the correction worked

Re-run the same query set after editing.

Standards:

• Within three days: usually no change; the index has not refreshed. Do not panic
• Two to four weeks: retrieval engines (Perplexity) should start reflecting it
• One to three months: training-based answers may update

If the error persists after a month, either your edit missed the actual extraction point, or a higher-weighted source is still asserting the wrong fact. In that case, find out who else is saying it.

5. Long-term: make your correct statement the easiest to extract

Correction is reactive. Prevention is proactive.

Three proactive practices:

1. One core fact, one independently citable paragraph. Open the paragraph with a complete conclusion that makes sense without the preceding text.

2. Use tables for comparative facts. AI extracts tables noticeably more accurately than prose. Write the same thing as both a list and a table to raise correct-citation odds.

3. Standardize terminology. If your site calls a concept by several names ("unknown sources" vs. "install unknown apps"), AI treats them as different things. Unify the term and define it at first mention.

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6. Closing

Being described wrongly by AI is a side effect of your content being heavily used. It means you have entered the model's field of view — that is a good thing.

The real danger is being wrong about and not knowing it. Building a periodic citation-accuracy check matters more than writing more new articles.