The State of Eval Engineering Report - Q1 2026 

We’ve surveyed over 500 enterprise AI practitioners for the State of Eval Engineering Report, revealing what separates the best-performing AI teams from the rest and uncovering unexpected patterns in how they evaluate, build, and deploy AI systems.

This comprehensive research report examines the critical practices, measurement strategies, and cultural beliefs that drive exceptional outcomes in AI evals and AI reliability.

Download the State of Eval Engineering Report to learn:

  • Why elite teams measure more thoroughly than others, and why elite teams report more problems

  • How to implement consistent LLM evaluation methods, and why LLM-as-a-judge needs architectural solutions

  • Assuming "low-risk" backfires, and the measurement strategies that mitigate blind spots

  • Why team culture determines technical outcomes

Download the State of Eval Engineering Report to learn:

  • Why elite teams report more incidents, but have better AI reliability

  • How to implement consistent LLM evaluation methods, and why LLM-as-a-judge needs architectural solutions

  • Assuming "low-risk" backfires, and the measurement strategies that mitigate blind spots

  • Why team culture determines technical outcomes

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