AI Product Management
Ship AI features people actually trust — from opportunity to launch
10 phases. 58 lessons. 58 labs. 5 projects. Everything an AI PM needs that a traditional PM course skips: AI/ML literacy without the math, build-vs-buy and model-selection frameworks, data strategy, writing PRDs for probabilistic systems, eval design and LLM-as-judge, hands-on prompt engineering, responsible AI and governance, launch/rollout strategy, and AI-specific metrics. One feature idea carries through every project — opportunity assessment, PRD, eval plan, and responsible AI review — so y
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- Level: Beginner
Curriculum
- AI Product Management Foundations — What an AI PM actually does, when AI is (and isn't) the right solution, the AI product lifecycle, AI PM career paths & compensation, anatomy of the AI PM interview loop
- AI & ML Literacy for PMs — How ML models learn, LLM fundamentals (tokens, context, embeddings), model capabilities & limitations, reading model cards & benchmarks, RAG vs fine-tuning vs agents, data literacy for PMs
- Discovery & AI Product Strategy — Spotting AI opportunities, build vs buy vs fine-tune, model selection tradeoffs (cost/latency/quality), feasibility & risk triage, AI roadmapping
- Data Strategy for AI Products — Data audits, labeling & annotation & human feedback, data flywheels, privacy & consent & data governance, designing feedback collection UX
- Writing AI Product Requirements — The AI PRD, specifying non-deterministic behavior, prompt specs & prompt-as-interface, designing for failure, cost & latency budgeting in the PRD
- Evaluation & Quality for AI Products — Why AI products need eval systems, building an eval rubric, LLM-as-judge & automated evals, offline vs online evaluation, measuring hallucination/bias/safety, reading an eval dashboard
- Prompt Engineering & GenAI Feature Design — Prompt engineering fundamentals (hands-on), structured outputs & tool calling, designing AI agent products, multimodal product design, rapid prototyping AI features
- Responsible AI, Governance & Risk — Bias, fairness & responsible AI product decisions, transparency & explainability, AI regulation for PMs (EU AI Act, NIST AI RMF), AI incident response — the PM's role, security & abuse for AI products
- Launch, Metrics & Scaling AI Products — Rollout strategies (shadow, canary, gradual), defining AI product metrics & KPIs, monitoring model drift & post-launch quality, unit economics & ROI of AI features, cross-functional leadership
- Capstone & Career: Full AI Product Case Study — Building an AI PM portfolio, AI product sense interviews, technical fluency interviews for PMs, case study & strategy interviews, behavioral & cross-functional interviews, resume/LinkedIn/job search for AI PM roles
Skills You Will Learn
- AI Strategy
- PRDs
- Evaluation Design
- Responsible AI
- AI Metrics
- Stakeholder Management
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