Free Lesson

Your AI feature needs evals, not just analytics

Part of AI-Native Product Management Certification

Hosted by Dr. Marily Nika

Mon, Jun 8, 2026

8:00 PM UTC (30 minutes)

Virtual (Zoom)

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AI Product Management Bootcamp & Certification by AI Product Academy
Constantinos Neo @ Anthropic, Dr. Marily Nika, and Deb Liu, Former CEO @ Ancestry
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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


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