4 free questions from a 20-question segment. Get the full 100 →

Q81. How do you measure the success of an AI product? Walk me through your metrics framework.

• Type: Case | Difficulty: Medium
• What they're testing: Whether you have a layered framework for AI metrics or default to "accuracy and engagement."
• Framework hint:
  • Layer 1: Offline eval — accuracy, hallucination, faithfulness
  • Layer 2: In-product quality — accept rate, edit distance, retry
  • 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.