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Free Lesson
Create Agentic AI Evals from Scratch as a PM
45 min
Dec 4, 2026 12:00 PM
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What you'll learn
Define what "good" looks like for your agent
Turn user tasks into eval criteria (34) Translate user tasks into clear success criteria for an AI agent
Create your first eval dataset with Claude
Build a starter eval dataset from real tasks and edge cases, using Claude
Set up LLM-as-judge you can actually trust
Score agent outputs with rubrics and LLM-as-judge, and check the judge
Explain your eval strategy in an AI PM interview
Use eval results to make ship decisions and explain them in interviews
Why this topic matters
Your AI agent works in the demo, but is it good enough to ship? Most PMs leave evals to engineering and end up debating vibes, not results.
In this session, we’ll build an agent eval from scratch, live with Claude: define what “good” looks like, turn real tasks and failures into a test set, score outputs with rubrics and LLM judges, and make a ship decision. You’ll leave with an eval template for work and AI PM interviews.
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