MediumProductDataProduct case10 min to answer

How would you measure the success of a generative-AI product?

Asked at

Microsoft

1Give yourself 10 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around north star, task success, retention, harm metrics.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover north star, task success, retention, harm metrics. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

north star
task success
retention
harm metrics

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover north star, task success, retention, harm metrics. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

north star
task success
retention
harm metrics

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

north star

weight 30%

StrongExplains north star concretely and ties it to a decision or measurable outcome.

WeakMentions north star without implications, trade-offs, or evidence.

task success

weight 25%

StrongExplains task success concretely and ties it to a decision or measurable outcome.

WeakMentions task success without implications, trade-offs, or evidence.

retention

weight 25%

StrongExplains retention concretely and ties it to a decision or measurable outcome.

WeakMentions retention without implications, trade-offs, or evidence.

harm metrics

weight 20%

StrongExplains harm metrics concretely and ties it to a decision or measurable outcome.

WeakMentions harm metrics without implications, trade-offs, or evidence.

Then expect these follow-ups

Which metric could be gamed?
Tests: depth
What is your guardrail metric?
Tests: adaptability
How long before the metric becomes trustworthy?
Tests: adaptability

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