Hallucination triage: a playbook for fixing incorrect AI answers fast

Run hallucination triage like an incident response: capture the failing query and answer, classify the failure mode (retrieval, grounding, freshness, conflict, model), fix the source content and evidence, then re-verify across AI engines. Each step has an owner and an SLA so corrections ship within hours, not weeks.

TL;DR

When an AI engine gives a wrong answer about your brand, do not argue with the model — fix the inputs. This 7-step checklist captures the failing query, classifies it into one of five failure modes, ships a content or evidence fix, and re-verifies citations within 72 hours.

When to use this playbook

Use it whenever any of the following is true:

A user, journalist, sales rep, or competitor reports an AI answer that misstates a fact about your product, pricing, leadership, or policy.
An automated AI-visibility monitor (e.g. Semrush AI tracker, Perplexity citation tracker) flags a citation drift on a tracked query.
Internal QA finds that a tracked canonical question returns an outdated or fabricated response in ChatGPT, Perplexity, Google AI Overviews, Claude, or Copilot.

The five failure modes

Classifying the failure mode determines which fix to ship. Most retrieval-grounded AI failures map to one of these five modes:

Wrong evidence retrieved. The engine pulled an irrelevant page (often a competitor or outdated post).
Right evidence, wrong use. The engine retrieved your page but ignored or paraphrased it incorrectly.
Incomplete evidence. The engine retrieved partial information and filled gaps with guesses.
Conflicting evidence. The engine picked the wrong source among two contradictory pages on your site or the open web.
Outdated evidence. The engine retrieved correct-but-stale content (pricing, version, leadership, policy).

The 7-step triage checklist

Each step lists Owner, SLA, and the artefact you produce. Track everything in a single incident ticket.

☑️ Step 1 — Capture (Owner: Reporter • SLA: 30 min)

Record the exact query that produced the bad answer.
Save a screenshot or full transcript of the AI response, including any citations shown.
Note the engine, model version, and timestamp (e.g. ChatGPT GPT-5, Perplexity Sonar Pro, AI Overviews 2026-04 build).
Open an incident ticket with severity (P0-P3) and link to the artefacts.

☑️ Step 2 — Reproduce (Owner: GEO lead • SLA: 1 hour)

Re-run the query verbatim and 2 paraphrases on the same engine, in a logged-out session.
Re-run on the other 4 tracked engines.
Mark the bug reproducible, intermittent, or non-reproducible. Non-reproducible failures still get logged but exit at this step with a watch flag.

☑️ Step 3 — Classify (Owner: GEO lead • SLA: 2 hours)

Diff the AI answer against the canonical truth (your source-of-truth doc, pricing page, or policy).
Identify the failure mode (one of the five above). If multiple, list the dominant one and any secondaries.
Identify the canonical_concept_id affected. If none exists yet, create one — hallucinations on un-IDed concepts are an authoring gap, not just an AI failure.

☑️ Step 4 — Fix the source (Owner: Content writer • SLA: 4 hours)

Match the fix to the failure mode:

Wrong evidence retrieved → Ship a citation-ready page with a stronger answer block (definition + 1-2 evidence sentences) and route inbound links to it.
Right evidence, wrong use → Tighten the answer block: move the canonical answer into the first 80 words, remove hedging, mark up with FAQPage schema if relevant.
Incomplete evidence → Add the missing facts directly under the canonical question heading; cite a primary source per fact.
Conflicting evidence → Pick one canonical page, redirect or de-index the duplicates, and update internal links to point at the survivor.
Outdated evidence → Update updated_at, last_reviewed_at, and the visible "Last updated" stamp; bump version. Add a brief changelog block at the bottom of the page.

☑️ Step 5 — Strengthen evidence (Owner: Content writer • SLA: same as Step 4)

Add at least one primary source per affected claim (vendor docs, schema.org, peer-reviewed paper, government data).
Record each new source in the evidence ledger with: URL, retrieval date, supported claim, and confidence (high / medium / low).
Update Research Notes on the article row with the same entries.

☑️ Step 6 — Re-verify (Owner: GEO lead • SLA: 24-72 hours)

Wait for the engines to recrawl. Most engines refresh frequently-updated pages within 24-72 hours; some (Perplexity Sonar) refresh in minutes for tracked URLs.
Re-run the original query and the 2 paraphrases on all 5 engines.
Mark the incident resolved only when at least 3 of 5 engines now cite the corrected page or return the corrected answer.
If still failing after 72 hours, escalate to Step 7.

☑️ Step 7 — Escalate or accept (Owner: GEO lead + Eng • SLA: 7 days)

If the engine continues to misstate after content/evidence fixes, file a content correction request through the engine's official channel (OpenAI feedback, Perplexity correction form, Google AI Overviews feedback).
For brand-safety incidents, prepare a public-facing statement before requesting correction.
Document the engine, ticket id, and date in the ticket. Set a 14-day follow-up.

Definition of done

A hallucination ticket is closed only when all four of these are true:

Failure mode classified and recorded.
Source content + evidence updated and indexed.
At least 3 of 5 tracked engines now return the corrected answer or cite the corrected page.
Ticket linked to the affected canonical_concept_id so the next audit cycle picks it up.

Anti-patterns

Arguing with the model. Prompting the AI "are you sure?" does not fix the underlying retrieval; fix the source.
Stuffing keywords. Adding the canonical question 10 times to a page makes extraction worse, not better.
Deleting the bad page silently. Without a redirect, you lose the URL signal entirely. Redirect or rewrite.
Treating a single hallucination as a one-off. Hallucinations cluster around weak canonical_concept_ids. Run a cluster audit when you see two within 14 days.

FAQ

Q: How fast can a hallucination realistically be fixed across AI engines?

Most source-content fixes propagate within 24-72 hours for ChatGPT, Perplexity, and Google AI Overviews when the fix lands on a frequently-crawled URL with a strong canonical signal. Outdated-evidence fixes propagate fastest; wrong-page-retrieved fixes can take a week if the competing page has stronger authority.

Q: Should I file a feedback ticket with the AI engine before fixing my content?

No. Fix the source first — most engines re-retrieve from the open web on each query, so a content fix often resolves the incident without a ticket. File a feedback ticket only when the engine continues to misstate after 72 hours.

Q: How do I tell the difference between a model hallucination and a retrieval failure?

Look at the citations the AI answer shows. If it cites no source or an unrelated source, it is a retrieval or grounding failure (modes 1-3). If it cites the right page but misquotes it, it is mode 2 (right evidence, wrong use) — fix the answer block on that page.

Q: What is an evidence ledger and why is it required?

An evidence ledger is a row-level record of every primary source that grounds a claim on the page, with retrieval date and confidence. It is required because hallucination triage often resurfaces the same disputed facts; the ledger lets the next responder skip re-research.

Q: Can this playbook be automated?

Steps 1, 2, and 6 can be automated with an AI-visibility monitor that watches a tracked query set. Steps 3-5 (classify, fix, strengthen) currently require a human content owner because they decide canonical truth and edit narrative; partial automation (suggesting failure mode and draft fix) is reasonable and recommended.