The bar at this level
PM fundamentals plus evals, model limits, trust, privacy, cost, and fallback design.
PrepOS readiness threshold: 3.9 (on the 5-point open rubric).
What changes vs the level below
- Adds the AI product judgment round on top of PM fundamentals
- Eval design replaces vague AI hand-waving
- Cost and latency trade-offs become first-class concerns
Where to focus practice
- Designing eval sets and regression checks for LLM features
- Hallucination mitigation and human-fallback paths
- Token-cost / latency / quality trade-off articulation
Questions calibrated for AI PM
- Search relevance for a niche video catalog
- Match quality on a tutoring marketplace
- Design an AI-native travel planner
- AI tutor for adult language learners
- AI-native CRM for solo founders
- Add AI to meal planning without breaking trust
- Engagement vs. wellbeing
- Exec dashboard for an AI product
- Design evals for an AI support assistant
- Eval framework for an AI code-review assistant
- When is human-in-the-loop required?
- Recovering from a viral bad answer
- Latency vs. cost vs. quality on AI chat
- Fallback when the model is uncertain
- Should a startup fine-tune on customer data?
- A new jailbreak goes viral
- Launch criteria for an AI sales assistant
- Evals for an AI K-12 math tutor
- Third-party model vs. self-host
- Tell me about a disagreement with engineering
- Raising an ethical concern
- Should a B2B SaaS product add an AI copilot?
- Bundle or charge separately for AI?
- Moat for an AI-native note-taking app
- Compute cost for an AI feature
+ 16 more in the full bank.
Practice for AI PM in PrepOS
Open the practice simulator, set Target level to AI PM, and the adaptive queue will weight reps for this calibration.
Practice for AI PM →