Develop the specific shape of AI product management: eval-driven development, inference-cost unit economics, hallucination as a product problem rather than a bug to file upstream, prompts as product surface, the moat question asked honestly, and the operational reality of building on infrastructure that ships new capabilities every quarter. By the end you can design an eval suite, model inference economics, and operate an AI feature at the standard the category demands. Eval suites are integration tests — inputs, expected outputs, pass/fail, gating on every change — and AI products ship without them for the same reason early-stage apps ship without tests: the cost of not doing it is invisible until it isn't. Inference cost is the N+1 query: every model call is a priced query against an external system, and caching, batching, and routing cheap tasks to cheap models are query optimization with a monthly bill attached. Prompts are DSL design — deliberate craft, read more than written, small changes with compounding consequences — and the discipline is versioning, reviewing, and evaluating them like the product artifacts they are. Hallucination handling is graceful degradation: treat "the model produced wrong data" like "the downstream service returned garbage" — a handleable pattern, not a fixed fate. The model-version dependency is the framework upgrade: pin, test against the new version, abstract the seams. And the moat question is the architecture decision record: which capabilities are load-bearing in your favor, which are commodity, which are upstream and out of your control. You answer this well in code review; the skill transfers to strategy the moment you let it.
This course unlocks once you've finished its prerequisite. Open prerequisite →
This is the strangest module for me to write, because I am a product made by Anthropic, writing about how to manage products built on systems like myself, and the recursion is uns…
There is no Inspired for AI PMs and no Crossing the Chasm for foundation-model products. The field is being invented in real time by practitioners writing blogs and giving talks, …
Approach: Essential
Approach: Essential
Approach: Important
Approach: Important
Approach: Important
Applied to your most operationally active AI product or feature. If you ship no AI surface at all, defer this module and return when you do — don't do it hypothetically.
8 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.