Product and Commercial · Available
AI Native Product Manager
TAI-APMProduct management when the feature is non-deterministic — requirements, model choice, AI UX, quality measurement and launch.
- Effort
- 10 weeks
- Level
- Professional
- Passing
- 70%
Competency blueprint
Assessment weighting is published up front — 20% knowledge, 30% labs, 20% portfolio and 30% capstone.
Knowledge checks
20%A quiz per module, marked immediately.
Practical labs
30%Work done in the tools, submitted as evidence.
Portfolio
20%The assignments that accumulate across the track.
Capstone
30%A realistic brief, graded against a rubric by a human.
Assessment blueprint
Knowledge
20%
Labs
30%
Portfolio
20%
Capstone
30%
70% overall to pass. The capstone must pass independently of every other score. Two retakes per assessment window.
Capstone
Capstone details are published inside the programme.
Modules
- AI product fundamentals
- Identifying AI opportunities
- User discovery for AI features
- Writing AI product requirements
- Choosing models
- Prototyping
- AI UX
- Evaluating model quality
- Cost, latency and reliability tradeoffs
- Responsible AI in product
- Launching and iterating