AI Product Manager: Prep Plan

Two dated plans that sequence the rounds, questions, resources, and courses into a path. Pick the 4-week plan if you have runway, or the 1-week crunch if the loop is close. Both assume you already have general PM fundamentals and are adding the AI-specific layer.

How to use it: each day has a learn block and a practice block. Do the practice out loud or in writing, because the interview is spoken. Keep a running portfolio of 4 stories (one you shipped and measured, one you killed, one where you pushed back on AI, one degradation or fairness incident) and refine it all the way through.

Timeline

4-week plan. Adds the AI-specific layer on top of general PM fundamentals.

  Week 1 |== Foundations + capability judgment ====|
  Week 2 |== Evaluation, metrics, and cost =========|
  Week 3 |== Agents, technical literacy, responsible AI ==|
  Week 4 |== Strategy, execution, behavioral =======| --> LOOP

4-Week Plan (about 1 to 2 hours per day)

Week 1: Foundations and capability judgment

Day 1. Read the README and rounds.md end to end. Map the loop and note which rounds scare you most. Skim the GenAI roadmap.
Day 2. Learn: Foundations topic and start Generative AI for Beginners (lessons 1 to 4). Practice: the Capability judgment and product sense questions out loud.
Day 3. Learn: Prompting and RAG topics. Practice: the Model and architecture literacy questions. Explain RAG and embeddings to an imaginary non-technical exec in 60 seconds each.
Day 4. Learn: People + AI Guidebook chapters on mental models, feedback, and errors. Practice: the Capability judgment and product sense questions (product sense and trust).
Day 5. Learn: Emerging Architectures for LLM Applications (a16z) and A practical guide to building agents (OpenAI). Practice: sketch the architecture of an AI feature you know on paper.
Weekend. First full mock: an AI product-sense prompt from rounds.md round 2, timed at 45 minutes, self-recorded. Draft 2 of your 4 portfolio stories.

Week 2: Evaluation, metrics, and cost

Day 6-7. Learn: AI Evals for Everyone (this is the highest-leverage material for the whole loop). Practice: the Evaluation and metrics questions.
Day 8. Learn: Evidently AI LLM guide and OpenAI Evals guide. Practice: the Evaluation and metrics questions (LLM-as-judge, agent metrics, offline vs online).
Day 9. Learn: cost and latency. Reread rounds.md round 3 and the Production topic. Practice: the Cost, latency, and unit economics questions (unit economics, latency, model sizing). Do the arithmetic on a real cost-per-call example.
Day 10. Learn: AI Evaluation 2025 research table and RAG research table. Practice: the RAG diagnosis question (in Model and architecture literacy) and the silent-degradation question (in Evaluation and metrics) as full 3-minute answers.
Weekend. Second mock: the metrics and technical-depth round, 45 minutes, with a friend playing a data scientist who keeps asking "how did you measure that." Draft your remaining 2 portfolio stories.

Week 3: Agents, technical literacy, and responsible AI

Day 11-12. Learn: Agents topic, Building Effective Agents (Anthropic), and Agentic AI Crash Course. Practice: the agent questions in Model and architecture literacy and Responsible AI, safety, and agents.
Day 13. Learn: Effective Context Engineering and MCP intro. Practice: the context-engineering and MCP questions in Model and architecture literacy.
Day 14. Learn: Safety and Security topic and Securing Agentic AI Systems. Practice: the Responsible AI, safety, and agents questions.
Day 15. Learn: NIST AI RMF and the EU AI Act explorer. Practice: the Responsible AI, safety, and agents questions (responsible AI as launch gates).
Day 16. Learn: Fine-tuning topic and Fine-tuning 101. Practice: the fine-tuning questions in Model and architecture literacy (build vs fine-tune vs custom).
Weekend. Third mock: the AI technical-literacy round with someone technical, then the guardrails-for-an-agent design question live.

Week 4: Strategy, execution, behavioral, and integration

Day 17. Learn: State of AI 2025 report and The AI PM Roadmap 2026. Practice: the Business and strategy questions (build vs buy, pricing, moats, roadmap, ROI).
Day 18. Learn: rounds.md round 6 (cross-functional) and the company interview guides for your target (Microsoft, OpenAI, or Sierra). Practice: the probabilistic-PRD question in Execution and behavioral and a build-vs-buy decision live.
Day 19. Behavioral day. Practice: the Execution and behavioral questions plus the rounds.md round 5 prompts. Tighten all 4 portfolio stories into STAR with real numbers. Make sure you have a graveyard story.
Day 20. Optional prototype round: build a small demo of one feature with an AI builder tool (Cursor, v0, or similar), narrating tradeoffs. See rounds.md round 7.
Day 21. Full loop simulation: product sense, metrics, technical, behavioral back to back. Note weak spots and reread only those questions and topics.
Ongoing. Skim the 2026 papers folder so you have one or two fresh references to mention.

1-Week Crunch Plan (about 2 to 3 hours per day)

For a loop that is days away. Triage hard: the metrics and technical rounds fail candidates most, so weight them.

Day 1. Read README and rounds.md. Work the Capability judgment and product sense questions out loud. Draft your 4 portfolio stories.
Day 2. Do the core of AI Evals for Everyone. Work the Evaluation and metrics questions. This is the highest-value day.
Day 3. Read Building Effective Agents and the Agents topic. Work the Model and architecture literacy questions. Practice the two 60-second explainer answers (RAG, embeddings).
Day 4. Cost and responsible AI. Work the Cost, latency, and unit economics and Responsible AI, safety, and agents questions. Skim the People + AI Guidebook errors and trust chapters.
Day 5. Strategy and execution. Work the Business and strategy and Execution and behavioral questions. Read the company interview guide for your target. Tighten STAR stories.
Day 6. Two mocks: one AI product-sense round and one metrics round, timed, out loud, ideally with a friend drilling "how did you measure that."
Day 7. Light review of only your weak spots. Reread the rounds.md "common mistakes" for each round. Rest so you are sharp.

Readiness checklist (green before you walk in)

I can decide whether a problem should use AI at all, and defend it.
I can design an AI feature end to end including the unhappy path (fallback, confidence, citations, undo).
I can describe an eval harness: offline set and regression suite versus online signals, and name specific metrics (recall@k, faithfulness, containment, escalation, edit rate).
I can define hallucination, measure it, and name a launch gate.
I can do the unit-economics arithmetic and reason about p95 latency.
I can explain RAG, embeddings, agents, reasoning models, and MCP in plain language, and say when each fits.
I can design guardrails for an agent (scoped permissions, human confirmation, limits, kill switch, red-team).
I have 4 crisp portfolio stories with numbers, including one AI feature I killed.

When you can check all 8, you are ready. Back to the README.

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