Hallucination Escalation and Correction Path (QA)

Hallucination Escalation and Correction Path (QA)

1. 01

   Capture the exact AI output verbatim before it changes

   Screenshot the full response, note the model name and version, the prompt that produced it, and the timestamp. Model outputs shift between sessions, so an uncaptured claim cannot be reproduced or disputed later.

2. 02

   Classify the defect as fabrication, stale fact, or misattribution

   A fabricated claim (a service the client never offered) needs a different correction path than a stale fact (an old address) or a misattribution (a competitor's award credited to the client). The classification determines who owns the fix.

3. 03

   Trace the citation source the model leaned on

   Platforms such as LLM Pulse surface which URLs feed AI answers, so you can identify whether the bad claim originates from a client-owned page, a third-party directory, or a press item. Fixing the source is the only durable correction.

4. 04

   Score severity against client revenue exposure

   A wrong phone number on a local service brand costs calls within days; a misstated founding year rarely matters. Route anything touching pricing, safety, licensing, or legal standing to the client contact within 24 hours.

5. 05

   Correct the upstream source and log the change

   Update the offending page, submit a correction to the directory, or publish a clarifying page that answers the disputed question directly in the first 100 words. Record what changed and when, because re-crawls are not immediate.

6. 06

   Re-run the original prompt on a fixed schedule until the answer changes

   Check at day 7, day 30, and day 60. If the claim persists after two cycles, escalate to a content placement or suppression scope, which is where custom services like Reputation Rhino enter the engagement.

7. 07

   Report the incident and the correction to the client in writing

   Include the original output, the source traced, the fix applied, and the current answer state. Clients tolerate AI errors they can see being managed; they do not tolerate discovering them independently.