Already a PM, new to AI
Read it in order. The first eight lessons feel unusually technical for a PM course. Stay with them — every technical concept later becomes a PM decision. Skip the foundation and the decisions look arbitrary.
Everything you need to sit across from an AI engineer, read the JIRA of an AI feature, and make the calls a PM should own — from prompts and evals to cost, latency, and safety.
Most AI-PM courses stop at theory. This one is the practical layer — years of study and shipping AI features, distilled into the vocabulary and decision frames you need in the room.
Priced at ₹99 to be an easy yes. Buy it, use it, hand it to a teammate.
Read it in order. The first eight lessons feel unusually technical for a PM course. Stay with them — every technical concept later becomes a PM decision. Skip the foundation and the decisions look arbitrary.
Read it in order too. Lesson 18 (the AI PRD) and Lesson 16 (evaluation in production) are the two you can most directly adapt into a work sample for interviews. A written rubric and an AI PRD is worth more than a certificate.
The technical lessons will feel easy. The value for you is decision framing and failure modes. Skim Parts I through IV, spend real time on Parts V and VI.
Read it in order. Pay extra attention to Lessons 17 (unit economics), 19 (build vs buy vs self-host), and 22 (pricing and trust). Those three will determine whether your idea has a business under it.
In traditional software, the same input gives the same output. add(2, 3) always returns 5. In AI, the same input may give a different output every time, and even the correct output varies in tone, length, and wording.
This breaks several PM habits. QA pass/fail doesn't apply. You can't write “the system returns X” as an acceptance criterion. You write “the system returns a summary scoring ≥4/5 on faithfulness in 95% of cases.”
Bugs become statistical. A dropped quote is either one-off variance or a regression. Telling them apart needs evaluation infrastructure.
A written rubric for your feature's outputs, with a golden-set methodology you can defend.
Behavior under failure, statistical quality bars, escalation paths — the whole document.
Per-request unit economics with caching, batching, and cascade routing lines drawn.
A prompt-injection and misuse audit for your feature's real attack surface.
The consequential architecture call, defended in one page.
Citations, confidence states, "I don't know" behavior — designed on purpose.
A per-layer budget you agree with engineering, not one they hand you.
What to do the day a vendor updates the model on you.
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Buy AI PM Foundations · ₹99