AI Product Engineering
AI UX, failure recovery, product metrics beyond accuracy, experimentation, cost modeling and pricing, and feedback loops that compound.
- Chapters
- 6
- Hours
- 1
- Difficulty
- Intermediate
- 11.0intermediate
AI UX
Streaming as a trust signal, not a transport detail. The three failure modes (blank screen, mid-stream stall, unverifiable claim) and what to build for each.
- 11.1intermediate
Failure recovery UX
AI failures aren't edge cases. Designing the fallback cascade, retry affordances, and human handoffs so the failure you can't prevent doesn't destroy trust.
- 11.2intermediate
AI product metrics
Beyond accuracy. Task success, deflection vs. containment vs. resolution, escalation as a quality signal, time-to-completion, and AI-feature retention.
- 11.3advanced
Experimentation
A/B testing AI features when variance is 2-5x higher than deterministic features, the treatment can drift mid-experiment, and 'no significant effect' usually means underpowered.
- 11.4intermediate
Cost modeling and pricing
Why marginal cost matters again. Cost-per-task (not per-token), the four pricing architectures, and the math that says when a feature is too expensive to ship.
- 11.5advanced
Feedback and growth loops
Most teams have a log file, not a flywheel. The three preconditions for a working data loop, and why the loop itself (not the data) is the durable advantage.