Layer 3: User behavior — adoption, retention, completion
Layer 4: Business impact — revenue, cost saved, NPS
Layers must connect causally
Q83. Define hallucination rate. How would you measure and set a target for it?
Type: Case | Difficulty: Hard
What they're testing: Depth on a term everyone uses loosely. Can you operationalize it beyond hand-waving?
Framework hint:
Task-specific definition
Eval set with labeled ground truth
LLM-judge, human eval, or auto fact-check
Target based on cost of hallucination in context
Track direction/regression, not just level
Q94. Your CFO wants to know the ROI of your AI feature. How do you calculate it?
Type: Case | Difficulty: Medium
What they're testing: Business-side AI communication. Most PMs give hand-wavy answers to CFOs.
Framework hint:
Cost: infra, model, eng time, ops
Benefit: revenue lift, cost saved, retention
Attribution — causal share of outcome
Time horizon — one-time vs recurring
Range with honest uncertainty
Q100. Design a metric to detect if your AI model has silently regressed in production.
Type: Case | Difficulty: Hard
What they're testing: Post-launch AI ops. Silent regression is the AI PM nightmare — separates production-experienced from prototype PMs.
Framework hint:
Shadow eval on production traffic
User signals: accept, retry, escalation
Input distribution monitoring vs training
Downstream: completion, conversion trending
Alerting on leading indicators
🔓 16 more questions in this segment alone — including A/B testing non-deterministic outputs, agentic workflow metrics, and eval framework design. Get the full 100 on Gumroad →