Human reviewGuide6 min

Create a human review loop for generated recommendations

A product pattern for letting AI accelerate decisions while keeping accountable people in control.

Real example

Example: approve, edit, or reject generated renewal-risk summaries

An account team receives AI-generated renewal-risk summaries from support tickets and product usage. Some are accurate; others miss context only the account manager knows.

Render each recommendation as a draft. The reviewer can approve it, edit the summary, reject it with a reason, or request more evidence. Downstream CRM updates run only after approval.

The AI speeds up analysis, but the accountable human keeps control of customer-facing and revenue-impacting records.

Tutorial path

How to implement it

Step 01

Mark each generated recommendation as draft, approved, rejected, or needs evidence.

Step 02

Expose source material and model reasoning summary next to the editable fields.

Step 03

Let reviewers correct the output before approval instead of forcing a full rerun.

Step 04

Store reviewer decisions as training and evaluation examples for future changes.

Step 05

Send downstream notifications only after approval.

Checklist

Ready when these are true

Draft state exists

Reviewer can edit before approve

Override reason captured

Downstream action gated

Review metrics visible

Field notes

What matters in practice

Review loops work when the reviewer sees inputs, output, evidence, and consequences on one screen.

The system should record why people overrode or accepted the recommendation.

Review data becomes the best source for prompt and workflow improvement.

Avoid these mistakes

Common failure modes

Do not make review a side-channel in Slack or email.

Do not lose the original model output after a human edits it.

Do not optimize only for approval rate; correction and rejection reasons matter more.

Practical tip

The edit diff is valuable. It shows what the model systematically misses.

Apply this to a build