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How to add human review to AI workflows

Specify what is checked, who checks it and what happens when the answer is uncertain.

In brief

How do you add human review to an AI workflow?

Define the sources an AI tool may use, keep drafts separate from consequential actions, and name the person who approves the result. Give that reviewer specific checks and a route for missing or conflicting information. Record corrections so recurring errors can guide changes to the workflow.

By NMR Infotech · Practical guide · Updated

Define the source boundary

Specify which documents and systems may be used. A confident answer is not a substitute for an approved source.

Separate drafts from decisions

Label draft outputs and keep consequential actions behind a suitable approval step. Make the handoff visible to the responsible person.

Give reviewers a checklist

Check facts, numbers, permissions, completeness and whether the output answers the actual task. Adapt the checklist to the workflow.

Design an exception path

Let the workflow stop or escalate when information is missing, contradictory or outside its scope.

Record corrections

Track recurring failure patterns and revise prompts, retrieval, task scope or training. Review effort is part of the workflow’s cost.

This guide describes a general working approach. It does not claim that a particular tool or AI implementation guarantees a business outcome.

Apply the guidance

How can you put this guide into practice?

Choose one task owner and a small permitted example. Write down the expected output, who will review it and what would make the result unsuitable for use. Run the exercise, record corrections and decide what must change before repeating it.

Keep the first version easy to inspect. Do not treat the example as evidence that an entire process can be automated or that a particular tool is appropriate for every kind of information.

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