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AI PromptsCategoriesAI Governance

✅ Make Sure a Human Actually Reviewed It

Put a sensible process around how your organization uses AI, without stopping anyone getting work done. These prompts build the approval queue, the register of what is running, the log of what each tool actually did, and the policy an assistant checks submissions against. Written for the person who has been asked to own this and given no software to do it with.
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Make Sure a Human Actually Reviewed It

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Our policy says a human must review AI output above medium risk and I have no idea whether that happens. Build me a human oversight app. From the risk level of each approved use case, determine whether output requires human review before it is used, and where it does, block the output from being marked as used until a named reviewer has signed it off. Record the reviewer, the date and time, the decision of accepted, amended or rejected, and a short note on what was actually checked rather than just an approval click. Show me review volumes, average time to review, and the proportion amended or rejected, since a reviewer who approves everything instantly is not really reviewing. Add an AI agent that prepares each review by summarizing what the output claims, highlighting anything it cannot support from the source material, and listing the specific things worth checking, so the reviewer is helped rather than replaced. Automate two things: a reminder when an output is awaiting review, and a scheduled monthly report on review quality indicators. Three logins: a user who submits output, a reviewer who signs off, and an auditor with read only access.

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01 / Spark - intelligence awakens

Make Sure a Human Actually Reviewed It

Require and record human sign-off on AI output by risk level, blocking use until reviewed, with reviewer and timestamp.

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// the spark catches · intelligence awakens · the answer unfolds

Human oversight in a policy usually means nobody knows whether it happened. This makes the check a step in the work with a name and a timestamp on it.

What's Included

  • Sign-off required by risk level set from your own policy.
  • Reviewer, date, and decision recorded on the output itself.
  • Blocked until reviewed where the risk level demands it.
  • An assistant that prepares what the reviewer needs to check.

How To Use

  1. Make the check a step, not an expectation. Expectations are not evidence.
  2. Record what was actually checked, not just that it was approved.
  3. Set review requirements by risk level rather than applying them to everything.
  4. Watch approval speed. Instant approvals are not reviews.

Drive it from risk assessment, and evidence it in the execution log.

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