AI Output Review Checklist: What to Check Before You Use It

AI output tends to fail quietly. It reads well, the structure is tidy, and the errors are specific rather than obvious — a wrong number, a confident claim with no source, a requirement silently dropped. A short, repeatable review pass catches most of it.

The checklist

1. Facts

• Every number, date, name, and citation is one you can confirm from a source you trust.
• Any statistic without a source is removed or verified — not softened.
• Quotes are real quotes, not paraphrases presented as quotes.

2. Completeness

• Every instruction you gave is reflected in the output.
• Nothing important from the source material was dropped in summarizing.
• Caveats and conditions survived the rewrite.

3. Reasoning

• The recommendation follows from the evidence given, not from generic best practice.
• Trade-offs are stated, not smoothed over.
• You can explain the conclusion in your own words. If you cannot, do not send it.

4. Context and audience

• The tone matches the reader and the relationship.
• It reflects how your organization actually works, not a generic company.
• Confidential or internal detail is not included where it should not be.

5. Voice

• It sounds like you, not like a template.
• Filler openings and inflated adjectives are cut.
• The first sentence says something.

Setting a rule for yourself

Decide in advance which categories of work always get a full review: anything with numbers, anything that leaves your organization, anything a decision will be made from. For low-stakes internal drafts, a quick scan is enough. Being explicit about the tiers keeps the review from being either skipped or overdone.

What to do next

• Save the five headings somewhere you will see them while working.
• Apply the full checklist to the next AI-assisted document you send externally.
• If output keeps failing the completeness check, the fix is usually context — see the context guide.