Reviewing AI Output: A Tiered Checklist

Not every output deserves word-by-word proofreading. Sort content into three tiers by exposure and reversibility, match each to a review depth, and check the four places factual errors cluster.

Proofreading every AI output word by word gives back the time you saved; reviewing nothing means the risk eventually surfaces in the most expensive way. The workable answer is to tier by risk and spend review effort where it matters.

Sort by two questions

Ask two things about each kind of output: will anyone outside see it, and can a mistake be quietly undone?

High risk (external and irreversible): contract terms, customer quotes, published commitments. Word-by-word review plus a second reviewer.
Medium risk (external but retractable): social posts, email drafts, support reply templates. Verify facts and figures; wording can be used as produced.
Low risk (internal and reversible): meeting notes, internal summaries, research drafts. Spot-check, focusing on whether the conclusion points the right way.

Where factual errors cluster

When review time is limited, these four checks give the best return:

Numbers: amounts, percentages, dates, quantities. Models handle these least reliably and readers take them most literally.
Proper nouns: people, organisations, product versions. One wrong character makes it a different thing.
Causal claims: "because X, therefore Y" is often supplied by the model when the source implies no such link.
Absolute wording: "all", "first", "only", "guaranteed". In external copy these carry both factual and compliance risk.

Make review a step, not a habit

Give review an explicit node in the workflow: who reviews, against what, and where the sign-off is recorded. Review that depends on people remembering gets skipped when things are busy — which is exactly when errors appear.

Keep a traceable record

For high-risk output, retain the source material, the prompt used, and the reviewer with a timestamp. When something goes wrong you can tell whether the material, the prompt or the review was at fault, and the three have different fixes.

Summary

Tiering is not about lowering standards. It concentrates limited attention where mistakes actually cost something. Adjust the checklist to your team, but keep the order: tier first, then set depth.