If the model is asked to verify its own unsupported draft, start by preserving one representative example. It gives the investigation a concrete reference and makes the eventual correction easier to judge.
Find the likely cause
Repetition is treated as independent confirmation.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Use external evidence and human review for consequential factual claims.
Fact checking requires tracing important claims to evidence rather than asking the same model to reassure itself.
Check that the repair worked
Separate generated confidence from actual corroboration.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: aI cites sources that do not exist. Open each reference and confirm its title, author, and relevant passage.
Monitor the useful outcome
Track unsupported claims found before publication and recurring categories of factual error.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.