


Free Lesson
Validate Claude Code Analytics Output
Part of The AI Evaluation Handbook
60 min
Jun 17, 2026 11:00 AM
What you'll learn
Run a 4-layer validation on any AI analysis
Check structure, logic, business rules, and segment-level reversals before trusting the output.
Define evidence that supports a ship decision
For each step, specify what evidence would justify ship, iterate, or stop, and what “evidence” is misleading.
Catch errors AI analysis gets wrong
Learn the specific failure modes where AI confidently produces wrong answers and how to spot them.
Re-derive findings to confirm correctness
Use Claude Code to approach the same question a different way to measure both capability and reliability
Why this topic matters
AI analysis tools are confident even when they are wrong. Most people eyeball the output and move on. This lesson teaches a 4-layer validation stack so you can catch structural errors, logic mistakes, violated business rules, and hidden segment reversals before the wrong number hits a stakeholder deck.
You'll learn from

Shane Butler
Principal Data Scientist at Ontra

Sravya Madipalli
Senior DS Leader (Ex-Microsoft)

Hai Guan
Head of Data at Ontra, Ex-LinkedIn
Previously at Stripe, Nextdoor, PwC