The distinction shows up in what happens before and after generation. A product engineer turns ambiguity into a measurable constraint, chooses the tradeoff, instruments the result, and owns the failure mode in production. An AI-tool operator can produce a convincing implementation but often cannot explain which user behavior should change, what evidence would falsify the approach, or how the system degrades. I would evaluate candidates on that closed loop rather than on prompt fluency or raw output speed.
The distinction shows up in what happens before and after generation. A product engineer turns ambiguity into a measurable constraint, chooses the tradeoff, instruments the result, and owns the failure mode in production. An AI-tool operator can produce a convincing implementation but often cannot explain which user behavior should change, what evidence would falsify the approach, or how the system degrades. I would evaluate candidates on that closed loop rather than on prompt fluency or raw output speed.