Free Lesson
Design Evals Users Will Trust
Part of Evals for Everyone
45 min
Feb 18, 2026 12:00 PM
Virtual (Zoom)
In this video
What you'll learn
Distinguish model evals from product evals
Learn why benchmarks that look impressive on paper often fail in production, and what to measure instead
Design your evaluation framework
Apply a three-component structure (reference datasets, metrics, and scoring methods) that scales with your AI product
Create reference datasets before launch
Build evaluation datasets that catch real failure modes, not just the obvious ones
Why this topic matters
Most AI teams ship products that pass benchmarks but fail users. The gap between "model works" and "product works" is where careers and products stall. This session gives you the systematic approach used by production AI teams at companies like OpenAI and Google to evaluate what actually matters before your users find the problems first.
You'll learn from
Aishwarya Naresh Reganti
AI Founder & Advisor to F500 Leaders