Free Lesson
Your AI feature needs evals, not just analytics
Part of AI-Native Product Management Certification
30 min
Jun 8, 2026 4:00 PM
What you'll learn
Define what “good” means for an AI feature
Turn vague quality goals like “helpful,” “accurate,” or “personalized” into concrete evaluation criteria your team can a
Build a simple eval set from real user workflows
Create test cases from user journeys, edge cases, common failures, and expected outputs before you ship.
Connect evals to product analytics
Asses not only whether users clicked, but whether the AI output was useful, trusted, and safe.
Why this topic matters
AI teams are shipping features where the output is probabilistic. A dashboard can show adoption, retention, and drop-off, but it cannot tell you whether the model gave a good answer, hallucinated, or created user risk.
Evals help you test quality before launch. Analytics helps you understand behavior after launch. Together, they create the feedback loop that tell you what to improve next.
You'll learn from
Dr. Marily Nika
Gen AI PM Lead @ Google | ex-Meta, Fellow @ Harvard | TED AI Speaker | 40 u 40