How the Top 15% Approach AI Evals

72% of AI teams strongly believe comprehensive testing drives reliability, but only 15% achieve elite eval coverage. That 57-point execution gap separates teams shipping production AI from those stuck in prototype.

We surveyed over 500 enterprise AI practitioners to understand what elite teams do differently. The findings reveal four counterintuitive patterns: elite teams report MORE incidents yet achieve 2.2x better reliability, 93% struggle with LLM-as-judge consistency, "low-risk" assumptions backfire spectacularly, and belief predicts outcomes more than tooling.

In this webinar, our Head of Product Marketing, Paul Lacey, will present the data and key findings from the State of Eval Engineering Report. Then, he'll be joined by our Head of Developer Relations Gabriela de Queiroz and Co-founder/CPO Atin Sanyal for a fireside chat on what these patterns mean for production AI teams.

You'll learn:

• The five systematic practices that drive 2.2x better reliability outcomes

• Why elite teams report more incidents but ship better AI (and how to replicate this)

• How to bridge the 57-point gap between eval belief and execution

• Practical approaches to solving the LLM-as-judge consistency problem at scale