When aI research summaries invent missing survey responses, another broad rewrite or another batch of output may leave the underlying problem intact. Diagnose the relevant failure before changing the whole process.
Find the likely cause
The model fills gaps while organizing data.
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 only actual records and preserve missing values explicitly.
Original research is useful when the question, sample, method, and limitations are clear enough for readers to interpret the findings.
Check that the repair worked
Reconcile every reported count with the source dataset.
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: survey questions lead respondents toward a preferred answer. Use neutral questions and test them with a small pilot.
Monitor the useful outcome
Track methodological completeness and whether readers can distinguish observations from broader interpretations.
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.