The curriculum

The AI product management course that updates every week.

14 chapters and 102 lessons, from PM fundamentals to building AI products: discovery, the AI PRD, agents, evals, AI UX, trust, and landing the job. Every week we read live AI PM job posts and add, rewrite or retire lessons to match, so you learn what the role asks for now.

01 Making AI Product Decisions

Technical fluency and ML tradeoffs is the single strongest theme (90 percent of JDs, 77 companies), so the course opens here.

02 Using SQL and Logs

The report names SQL as the single most-named tool (27), ahead of APIs (24) and MCP (23), and flags that no course teaches it: this is called out twice, in the missing-material list and as one of the sharpest JD-versus-course disagreements

03 Prototyping AI Products With Coding Tools

PM-as-builder with agentic coding tools is a named gap and a named frontier.

04 Discovering and Sizing AI Opportunities

AI PM craft is the second strongest theme (88 percent, 73 companies) and building under ambiguity is 68 percent, both core.

05 Writing an AI PRD

Still inside the 88 percent craft theme, this chapter owns the artifact the report calls the differentiator from classic PM: the AI PRD must name risks, guardrails, and success metrics up front because the system is non-deterministic (Produ

06 Designing AI Agents and Tooling

Agents are 73 percent (68 companies) and enterprise AI agents are the single top product surface (11), so this is a central, mid-course chapter.

07 Designing AI UX and Oversight

AI UX and human oversight is 78 percent (71 companies), core, and central to the mid-course spine.

08 Designing Evals for AI Products

Evals are 53 percent (62 companies), real but middling against the top themes, and the most over-taught topic in the field.

09 Measuring AI Product Economics

Outcomes and metrics for AI is 62 percent and, tellingly, outranks evals (53 percent), yet the report says course metric content is dominated by eval mechanics: this is a deepen theme and a named gap.

10 Deploying AI Products in Enterprises

Enterprise and deployment is 79 percent (70 companies), enterprise AI agents is the top product surface, and the report calls this the largest single mismatch between JDs and courses: the field teaches greenfield capstones while the hiring

11 Building Multimodal AI Products

Multimodal is 34 percent (47 companies), which the report notes is broader than several topics that get far more course time, and it is a named gap: nearly absent across the field, with only Product Faculty's Sense layer touching it and no

12 Defining AI Guardrails and Agent Security

Safety, trust and governance appears in about 30 percent of AI PM postings (83 of 273, across 39 companies), and the report's verdict is right-size: keep guardrails and responsible-AI as a working PM skill (define guardrails in the PRD, require human approval for irreve

13 Leading Cross Functional AI Teams

The seniority mix is senior-heavy (97 senior, 92 mid, 55 staff), so the report's graduate is an experienced PM adding AI depth, and the staff-level moves are part of the target profile.

14 Interviewing for AI PM Roles

The report frames the entire course around a demonstrable, hands-on AI PM entering a senior-heavy market (97 senior, 92 mid, 55 staff), where the interview scores evidence of shipping over borrowed vocabulary and the probing question expose

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