A review habit for
AI-assisted work.

A polished answer and a well-supported answer are different things.

A useful review process looks beyond the tone of an output. It asks how the answer relates to the question, what supports it and what still needs to be established. The same discipline matters when AI contributes to the preparation.

Return to the original question

Check whether the output actually addresses the task. Look for changed assumptions, missing context and conclusions that go beyond the requested scope.

Read the underlying material

Open the original references and assess the passages being relied on. Check names, dates and citations against the source itself. If you cannot locate or confirm a reference, leave that uncertainty visible rather than treating it as established.

Separate facts, assumptions and interpretation

Make those categories easy to distinguish. A reviewer should be able to see where evidence ends and an inference begins. Identify any additional facts or expertise needed to evaluate the point.

Ask what might be missing

Review the boundaries of the material considered. Look for relevant limitations, counterpoints and changes that could affect the work. A confident tone should not close an unresolved question.

Keep responsibility clear

Agree who will review the output and what it may be used for. For a legal matter, rely on an appropriately qualified professional rather than treating an automated response as advice.

These are general workflow suggestions. They are not a legal opinion or a claim that a particular AI product meets a professional standard. The NIST AI Risk Management Framework offers broader background on AI risk management.

General information for planning and discussion. Not legal advice.