AI Answer Factual Correction Brief Template

AI Answer Factual Correction Brief (fill-in)

When to use: One row per wrong AI claim, with the incorrect assertion, authoritative owned source, update task and observation window, plus owners and a completion check.

AI answer factual correction brief

One brief per wrong claim an AI answer makes about you. Fill it the day you spot the error, and give every row an owner and a date.

Brief owner: [name] - Evidence folder: [link or path] - Brief prefix: [e.g. FIX-]

Per-brief detail (fill for every row)

• Correct fact: [one sentence, as it should read]
• Error type: [outdated / fabricated / misattributed / missing context]
• Severity: [high: legal, safety, price or policy / medium / low]
• Cited sources in the answer: [URLs the answer linked, if any]
• Owned source check: [does the page state the fact in plain text today? yes / no]
• Third-party sources to fix: [directory, review site or article repeating the error]
• Feedback sent: [route used + date, or none]
• Status: [open / source updated / observing / closed / escalated]

Next action

• Brief owner: [name] - action: confirm the correct fact with [legal / product / ops] by [date]
• Content owner: [name] - action: publish the update task on the owned source by [date]
• Outreach owner: [name] - action: ask each third-party source to correct the claim by [date]
• Observer: [name] - action: re-run the same prompt on [dates in the window] and log the result

Completion check

• The incorrect assertion is quoted exactly, with engine, prompt and capture date.
• The correct fact is confirmed by someone who owns it, not guessed.
• The owned source states the fact in plain text, and the update is live.
• Every row has an owner, a due date and an observation window with an end date.
• The brief records what happened, not what you hoped. No one promised the answer will change.

Error Triage Picker

When to use: Sort each wrong claim by type and severity, so you know where to fix it first and how fast to move.

Error triage picker

Sort the error before you write the update task. The type tells you where to fix it; the severity tells you how fast.

Severity

• High: legal, safety, health, price or refund policy. Start the same day and tell the brief owner.
• Medium: product facts, availability, locations. Start this week.
• Low: wording, minor detail. Batch with the next content update.

Before you triage, check the error is not a one-off. Look up your domain in the AI Visibility Checker to read recorded AI answers that mention it, then open a brief for each wrong claim.

Authoritative Owned Source Checklist

When to use: Checks that the page you point to states the correct fact clearly, is indexable and matches your other profiles.

Authoritative owned source checklist

The owned source is the one page you control that states the correct fact. Pick one URL per fact and make it easy to read and quote.

The page passes when

• The correct fact sits in plain HTML text near the top, not only in an image, PDF or script.
• It states one fact per sentence, with the condition next to it: region, plan, date.
• It shows when it was last updated.
• It is indexable: no noindex, not blocked in robots.txt, returns a 200 status.
• The same fact matches your other profiles: business listings, social bios, press kit.
• Any structured data on the page matches the visible text.

Fields

• Owned source URL: [full URL]
• Fact as written on the page: [quote]
• Last updated shown: [yes + date / no]
• Indexable: [yes / no + reason]
• Other places to align: [list]

If the fact lives on an About or Organization page, generate matching JSON-LD with the Schema Markup Generator. Organization schema is on paid plans; FAQ and Article are free. Keep it identical to the visible text.

Checking the same facts across many profiles? Use the Brand Entity Consistency Audit Template alongside this brief.

Platform Feedback Routes (checked November 2026)

When to use: The Google AI Overviews and OpenAI feedback routes as their own pages describe them, plus a log for what you sent.

Platform feedback routes (checked 14 November 2026)

Feedback is an extra step, not the fix. It tells the platform about the error. It does not promise a reply, a correction or a timeline.

Google AI Overviews

• At the bottom of the overview, click thumbs down if the overview is inaccurate, then Report a problem and choose the category that best describes your issue.
• Add details in the text field. Quote the wrong claim and give your owned source URL.
• Google's help page says the feedback includes your recent search query and its results, and is used to improve AI Overviews.
• Source: Google Search Help, "Find information in faster & easier ways with AI Overviews in Google Search" (support.google.com/websearch/answer/14901683).

OpenAI models

• OpenAI runs a chat model feedback form (openai.com/form/chat-model-feedback) for cases where a model fell short.
• The page says submissions help its research team improve models, and you agree OpenAI may use the data for training and evaluations.
• It does not promise a reply or a change.

Log every submission

• Route: [name + URL]
• Date sent: [YYYY-MM-DD]
• Text sent: [paste it]
• Screenshot of the confirmation: [file name]
• Reply received: [none / date + summary]

Routes change. Re-check each platform's own help page before you send, and record the URL you used.

Observation Window Log

When to use: Re-run the same prompt on set dates after the update, record what the answer says, and decide at the window end.

Observation window log

After the owned source is updated, watch the same prompt for a fixed window. Record what you see. Do not count a single good run as a fix.

Window start: [date the update went live] - Window end: [date] - Re-check days: [e.g. every Monday]

At window end, decide

• Corrected on every re-check (of the window or its one extension): close the brief and keep the log.
• Mixed: extend the window once and check which sources the answer still cites.
• Still wrong: look at the cited third-party sources, fix those, and escalate high-severity errors to the brief owner.

For a fuller re-run routine across many prompts, pair this with the AI Visibility Repeated-Run Log Template.

Spreadsheet Header (CSV)

When to use: A paste-ready header row and allowed values to run correction briefs in Google Sheets or Excel.

Paste-ready spreadsheet header

Paste this row into cell A1 of Google Sheets or Excel, then split text to columns by comma. One row per wrong claim.

brief_id,engine,model,prompt,capture_date,incorrect_assertion,correct_fact,error_type,severity,cited_sources,owned_source_url,update_task,update_owner,update_due,update_live_date,third_party_sources,feedback_route,feedback_date,window_start,window_end,recheck_days,last_result,status,brief_owner,notes

Allowed values

• error_type: outdated, fabricated, misattributed, missing_context
• severity: high, medium, low
• last_result: still_wrong, corrected, mixed, not_mentioned
• status: open, source_updated, observing, closed, escalated

Sheet tips

• Highlight rows where owned_source_url or update_owner is empty. That brief cannot move.
• Highlight rows past window_end that are still observing.
• Sort by severity, then capture_date, for the weekly review.
• Save the screenshot and raw answer text by brief_id. The AI Citation Evidence Archive Template shows a capture routine.

Worked Example (fictional Plinthwell garden studios)

When to use: One filled brief for an invented garden studio maker, from the wrong warranty claim to a closed, logged result.

Worked example: Plinthwell garden studios

Example only. Plinthwell is an invented maker of garden studios. The prompts, URLs, names, dates and claims are made up to show a filled-in brief.

Brief owner: marketing lead - Evidence folder: /evidence/ai-corrections/ - Brief prefix: FIX-

How the brief was filled

• Correct fact: confirmed by the operations manager from the signed warranty terms: 10 years, UK only.
• Error type: outdated. An old reseller listing still showed a retired 25-year offer.
• Severity: high, because it is a warranty promise.
• Third-party fix: outreach owner asked the reseller to update the listing on 2026-09-30.
• Feedback sent: thumbs down (inaccurate), then Report a problem, with the warranty URL, on 2026-09-28.

Observation log

• 2026-10-06: still wrong, reseller page cited.
• 2026-10-13: mixed, both numbers shown.
• 2026-10-20 and 2026-10-27: corrected, warranty page cited.
• Window end 2026-10-31: one mixed result, so the window was extended once to 2026-11-10.
• 2026-11-03 and 2026-11-10: corrected. Brief closed, log kept, re-check set for next quarter.

Why this works

• The fix started on the owned page and the source the answer cited, not only with feedback.
• The team wrote down a mixed result and extended the window once, instead of calling it fixed early.
• Nobody claimed the feedback caused the change. The log shows what was seen, not why.

Want help building a correction process for AI answers? Book a free consultation with Anatolii Ulitovskyi, or see our SEO and GEO services.