Lessons
1Defining success for AI products46 min read
An AI feature is a distribution, not a binary — so “done” stops being a checkbox. The deterministic-to-distributional shift, eval-driven definitions of done written before code, the rule-based baseline test, and the “the model is 85% accurate — do you ship?” question that opens every AI PM case round.
- →AI Success Criteria
2Product KPIs + model-quality metrics47 min read
The metric stack — product/business outcomes on top, model-quality in the middle, system reliability at the base — plus the divergence alarm that catches a model silently degrading while your North Star climbs. With named-company stacks (GitHub Copilot/Accenture, Notion) and the metrics that mislead.
- →Product and Model Metrics
- →Quality and Safety Measurement
3Offline, online & human evaluation48 min read
The three evaluation modalities and the right allocation between them, the offline regression / sampled-production / human-calibration split, LLM-as-judge as the only thing that scales — and its systematic traps (position bias, verbosity bias, self-preference) plus how elite teams calibrate it against human golden sets.
- →Layered Evaluation
- →Quality and Safety Measurement
4Responsible AI & risk49 min read
The NIST AI RMF (GOVERN/MAP/MEASURE/MANAGE) and its GenAI profile as the spine, turning risk frameworks into launch artifacts, multi-layer guardrails (NeMo’s five rail types), and red-teaming a probabilistic product — with the convergent “Risk Report” every frontier lab now ships.
- →Responsible AI Controls
- →Quality and Safety Measurement
5Launch gates & incident handling48 min read
Go/no-go gates with named owners and binary pass criteria, shadow → A/B → canary → full as the rollout order, the rehearsed rollback that is a P0 launch deliverable, AI-specific incident response (Gemini, Air Canada), and the four-family post-launch monitoring that catches drift.
- →Launch and Incident Readiness
- →AI Success Criteria
6Capstone: eval suite + launch plan50 min read
The whole track becomes one deliverable: given an AI feature, write the eval suite AND the launch plan exactly as a PM case round expects — success criteria, the metric stack, the offline/online/human eval mix, the responsible-AI gates, and the rollout/rollback/incident plan, assembled into one defensible artifact you can whiteboard end to end.
- →AI Success Criteria
- →Layered Evaluation
- →Launch and Incident Readiness
Skills in this course
- 01AI Success CriteriaDefine measurable quality and failure thresholds before build and launch.
- 02Product and Model MetricsConnect product outcomes, model quality, and system reliability without hiding divergence.
- 03Quality and Safety MeasurementSet critical quality floors and safety limits for probabilistic behavior.
- 04Layered EvaluationCombine offline, sampled online, human, and calibrated model evaluation.
- 05Responsible AI ControlsTranslate risk and red-team findings into owners, controls, and launch evidence.
- 06Launch and Incident ReadinessUse staged rollout, reliability gates, rollback, and incident response.