Home › Courses › 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 you graduate with a portfolio case study, not just quiz scores.
10 phases · 58 lessons · 58 labs · 5 projects
No prerequisites — this is a starting track.
AI Strategy · PRDs · Evaluation Design · Responsible AI · AI Metrics · Stakeholder Management
OpenAI Platform · Anthropic Console · Langfuse · Braintrust · DeepEval · Amplitude · Productboard · Figma · Linear · Notion
An AI PM is two different jobs wearing one title, and the gap between them is real. At one end: applied-AI-features-bolted-onto-an-existing-product work — adding a summarization button, a chatbot, a recommendation tweak. At the other: core AI/ML product work — owning a model's behavior, its eval suite, its data pipeline, and its rollout. Both are called "AI Product Manager." This course teaches the skills that transfer across both, and helps you figure out which one you're actually interviewing for.
You write PRDs that define desired behavior instead of exact specs, because the system is probabilistic, not deterministic. You build eval rubrics and read eval dashboards to decide if a feature is ready to ship. You partner with ML engineers and data scientists on model selection, data quality, and retraining cadence. You monitor drift, bias, and cost after launch — work a traditional PM never touches, because a traditional feature doesn't get worse on its own after it ships.
AI PMs earn 20-40% more than equivalent traditional PM roles because there's an acute shortage of PMs who can hold a real technical conversation with an ML team, write a spec for a system that's sometimes wrong, and make a defensible ship/no-ship call from an eval dashboard. That is a specific, learnable skill set — not a personality trait — and it is exactly what this course builds.
It is not a coding bootcamp — you will not become an ML engineer. It is not a "prompt engineering" course pretending to be a career program — prompting is one phase out of ten. It is a product management course, built end to end around the one thing that makes AI products different: the output is a probability distribution, not a guarantee, and every artifact you produce — PRD, eval plan, launch plan — has to account for that.
General product management literacy — writing a PRD, running stakeholder conversations, basic prioritization — is assumed, not taught here. No coding or ML math is required: if you can write clearly, think in tradeoffs, and are comfortable with basic spreadsheet math, Phase 1 teaches all the AI/ML literacy you need from scratch. If you are completely new to product management itself (never written any PRD, never run a sprint), get general PM fundamentals first — this course is built entirely around the AI-specific delta on top of that, not a PM 101.
No. The labs use short Python scripts as scaffolding to generate real PM deliverables — PRDs, eval rubrics, cost models, prioritization scores — but you are never expected to write production code. The skill being taught is PM judgment, not engineering.
Every phase here is AI-specific: PRDs for non-deterministic systems, eval design and LLM-as-judge, model selection tradeoffs, responsible AI as a product decision, and drift monitoring. A generic PM course teaches roadmaps and user interviews and adds a lecture on ChatGPT; this course is built entirely around what changes when your product's output is probabilistic.
70–90 hours of structured content across 10 phases. Most learners complete it in 2–3 months at 8–10 hours per week. Phases 0–1 are free and can be finished in a weekend to see if the role is the right fit before committing further.
AI Product Manager, ML/Applied AI Product Manager, GenAI/LLM Product Manager, AI Platform/Infrastructure PM, Responsible AI/Trust & Safety PM, and (with strong prior PM experience) frontier-lab PM roles at companies like Anthropic, OpenAI, and Google DeepMind.
Yes, in the sense that general PM craft — writing any PRD, running prioritization, working with engineering and design — is assumed background, not covered here. This is deliberate: see the "generic PM course with an AI module" question above. If you're transitioning from engineering or data science with no PM experience at all, pair this with a general PM primer first. If you're already a PM, you can move quickly through Phase 0 and focus on Phases 1–9, which is where the actual delta lives.
You have 24/7 access to a Claude-powered AI mentor that knows your progress and your work. It reviews your PRDs and eval rubrics, asks the follow-up questions a real hiring manager would, and helps you sharpen your reasoning without just handing you an answer.
Prompt Engineering · Latency · LLM-as-Judge · Agent · Fine-Tuning · Hallucination · RAG (Retrieval-Augmented Generation)
ML & AI Engineering · Responsible AI Engineering · Agentic AI Engineering
Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.
All courses · Pricing · About · FAQ · Glossary