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About this interview
A case interview on AI Product Management, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Apply the three-question 'should this even be AI' gate (genuine ambiguity, usable eval signal, tolerable cost-of-error) before any design work
Recommend a hybrid rules-first/model-fallback architecture that bounds model risk to the genuinely ambiguous subset of cases
Ground the AI-vs-rules gate decision in what data would prove the rules engine is actually failing, not intuition
Design a real fallback path for when the model's own confidence is also low
Calibrate the AI/no-AI recommendation to the actual cost of a wrong answer (e.g. churned enterprise account vs. minor delay)
What to expect
A banking-app dispute-routing case built to test the single most important AI PM gate: should a model even be involved here at all.
Practice reasoning from real data about where a language model earns its risk versus where a simple rule already does the job.
Includes a live exhibit on dispute volume and rule accuracy by category to ground your architecture in real numbers, not instinct.