The symptom is specific: aI audits recommend deleting pages they have not actually inspected. Start with the affected item and identify the decision or input that could produce this behavior.
Find the likely cause
The model infers quality from titles or sparse metadata.
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
Provide page content and require evidence for consequential recommendations.
An AI-assisted audit is useful when it helps organize evidence and prioritize human decisions about existing content.
Check that the repair worked
Open the affected page before approving deletion or consolidation.
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: audit scores obscure the reason a page needs work. Record the defect, supporting evidence, and proposed repair.
Monitor the useful outcome
Track confirmed issues, false positives, and completion of high-value corrections.
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.