AI Product Management
Scoping around uncertainty, quality bars, offline-to-online metric ladders and shipping under model drift.
6concepts
65flashcards
42minutes of reading
- 01 Pricing and Packaging an AI Feature Why marginal cost changes the pricing question, the three models in use and what each fails at, and the guardrails a pricing decision needs when usage is heavy-tailed.
- 02 Prototyping AI Features Before the Model Is Ready How Wizard-of-Oz studies, model-in-the-loop prototypes and eval sets written as specifications let a team learn what users need and what the model must do before anyone commits to building it, and the specific ways each method lies.
- 03 Scoping Under Capability Uncertainty Why you cannot specify an AI feature the way you specify software, the cheap experiments that resolve the uncertainty, and the scoping decisions that determine whether a feature is buildable at all.
- 04 Deciding Where the Human Stays The four automation levels available for any decision, the expected-cost calculation that selects between them, and why partial automation is usually right and usually hardest to design.
- 05 Metric Ladders from Offline to Online The chain from a model metric to a business outcome, why each link is weaker than teams assume, and how to validate the links rather than assuming them.
- 06 Shipping Under Model Drift Why an AI product's behaviour changes without a release, what that does to roadmaps and commitments, and the practices that make a product resilient to a dependency that moves on its own.