The short answer
Teams learn to set review triggers, display evidence, highlight uncertainty, limit workload, and record approval for consequential outputs.
The decision standard is simple: preserve the source, state the limits, and make the next human check obvious. A useful article should reduce uncertainty without pretending that every unknown has been resolved.
What to examine
“A human is in the loop” is not a control unless the reviewer has time, evidence, authority, and a clear rejection path. The handoff determines whether review changes outcomes.
Start with scope. Identify the product, account, audience, jurisdiction, data, and decision involved. Then separate what was directly observed from what a vendor, researcher, regulator, or commentator says. Record dates because AI products, access rules, and prices change quickly.
Rubber-stamp review can create false confidence. Measure override rates and sample approved work for missed errors.
A practical way to do it
- Define what the model drafts and what the reviewer decides.
- Put sources, changes, confidence, and prohibited content in the review view.
- Track approve, edit, reject, and escalate outcomes.
Keep the worksheet or test record with the draft. If another editor cannot reproduce the check from the saved evidence, the article is not ready.
Editorial guardrail
Do not fill a missing fact with a plausible sentence. Mark it as unknown, find a stronger source, narrow the claim, or remove it. Commentary belongs in a clearly labeled paragraph after the reported facts, not inside them.
Primary-source reading list
These are starting points, not automatic support for every sentence. The publishing editor must open each cited page and confirm the claim it supports on the day of review.
Bottom line
Teams learn to set review triggers, display evidence, highlight uncertainty, limit workload, and record approval for consequential outputs.