"A human will review it" is one of the most common answers to concerns about AI output. It's also one of the least specified.
Who reviews it? When? What exactly are they checking? What happens if they're busy, or tired, or reviewing their two hundredth item of the day? Without answers, the review step either becomes a bottleneck or a rubber stamp.
At WiZR, a lot of what Frontline did was generate content inside enterprise workflows. Review was part of the design from the start, and getting it right mattered as much as the quality of the model output itself.
Decide what the review is for
Reviews tend to serve one of a few purposes, and each needs a different design:
| Purpose | What the reviewer checks | Design implication |
|---|---|---|
| Accuracy | Facts, numbers, claims against a source | Show the source next to the output |
| Policy or compliance | Restricted terms, legal claims, brand rules | Highlight risky phrases, give a checklist |
| Quality and tone | Clarity, fit for audience | Allow fast edits, not just approve/reject |
| Accountability | A named person signs off | Record who approved and when |
If you try to do all four in one undifferentiated "review" screen, reviewers will skim. It helps to tell them what to focus on.
Put the checkpoint in the right place
Review before the output reaches anyone outside is the obvious place. But sometimes an earlier checkpoint works better. Reviewing an outline before generating a full document can save a lot of wasted effort. Reviewing the source material selection can prevent errors that would be hard to spot in the final text.
I'd ask: where is a mistake cheapest to catch, and where is it most likely to be caught?
Watch for rubber-stamping
When most outputs are fine, reviewers learn to approve quickly. That's natural. It also means the occasional bad output slips through, which is the one the review was meant to catch.
Things that help:
- Show what changed or what's uncertain. Point attention at the risky parts instead of asking someone to read everything equally.
- Keep volume realistic. If one person is reviewing far more than they can read properly, the review is theatre.
- Spot-check the reviewers. Occasionally sample approved items and see if anything was missed. It's about the process, not blaming people.
- Make rejection easy. If rejecting means writing a long explanation, people approve borderline items.
Microsoft's guidelines for human-AI interaction cover a lot of this, including making it easy to correct the system and to understand why it did something. 1
Measure the review itself
Track approval rate, edit rate, time spent per item, and what reviewers change. These tell you a lot:
- High edit rates on one type of content point to a fixable quality problem.
- Very short review times with very high approval might mean rubber-stamping.
- Common edits can become improvements to the prompt or source material.
The edits are the most useful data you'll get. They're a direct record of where the model falls short, written by the people who know the domain best. It's also worth looking at how the interface signals uncertainty so reviewers know where to look.
Plan for when to reduce review
Review costs time. If a particular type of output has been approved without changes for a long stretch, it may be reasonable to move it to sampling rather than full review.
I wouldn't do that by gut feel. Set the criteria in advance: how many outputs, what approval rate, what types of content. And keep full review for anything high-stakes, regardless of the numbers.
A review step that's designed well makes the AI more useful, because people trust what comes out of it. A badly designed one just slows everything down and gives everyone a false sense of safety.