WRITING / AI & PRODUCT PRACTICE
Human review in AI-assisted product workflows.
Where product judgement belongs when AI helps prepare requirements, analysis and work items.
A fluent output is not a complete decision.
AI can turn notes into structured text, but the team still needs to decide what problem is being solved, what belongs in scope and which trade-offs are acceptable. Good formatting should make those decisions easier to inspect, not hide their absence.
Review the intent before the work item.
Ask whether the output still reflects the user need and constraints. A detailed backlog is not useful if it faithfully breaks down the wrong problem. Keep the problem definition close to the generated requirements.
Make acceptance observable.
Acceptance criteria should describe behaviour that can be checked. Include failed saves, correction, retries and time boundaries when they affect the product rule. An example can expose a missing decision before development starts.
Separate generation, review and release.
Generating a PRD or a Jira item is not a release decision. Scope and priorities need product judgement; correctness needs verification; business acceptance and real-device checks may still require a person. Record what was checked and what remains uncertain.
Methodology and limits
This note explains my working principles and the representative workflow published on this site. It does not report a controlled study or a measured productivity improvement. Use it as a review framework, then evaluate it against your own task and evidence.