Strategy question
You need to introduce eval gates from prototype to GA to steady-state monitoring across teams you do not manage directly. How would you set hard vs. advisory gates, assign ownership for non-negotiable floors like critical-error rates, and drive adoption across pods without slowing shipping velocity?
- Abridge
- Strategy
- Hard
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What this question tests
Tests the ability to design an eval gate system that enforces safety without becoming a bottleneck across teams the candidate does not manage.
How to approach it
- Define a small set of hard gates, like critical error rate and PHI leakage, that no pod can bypass regardless of stage.
- Make most other checks advisory, giving pod leads discretion to ship with a documented rationale if they miss a soft threshold.
- Assign a single accountable owner, likely a central eval or trust team, for setting and revising the non negotiable floors.
- Build the gates into the CI or release pipeline so they run automatically instead of requiring manual sign off from a central team.
- Publish pass rates per pod so social pressure and leadership visibility drive adoption without you personally chasing every team.
- Pilot the gate system with one or two willing pods first, then use their velocity data to convince the rest.
What a strong answer includes
- Separates hard gates, non negotiable, from advisory ones, judgment calls, so teams are not blocked on subjective calls.
- Automates the gate in the pipeline rather than relying on manual review, which is what actually drives adoption without slowing shipping.
- Uses evidence from an early pilot pod to make the case rather than mandating adoption top down.
Common mistakes
- Making every check a hard gate, which slows shipping so much that teams find ways around it.
- No single owner for the floors, so gates drift or get quietly relaxed under deadline pressure.
Likely follow-up questions
- How would you handle a pod that consistently ships despite failing an advisory gate?
- Who decides when a gate should move from advisory to hard?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop