Eval Engineering is an open knowledge base exploring how to systematically evaluate, monitor, and govern AI systems in production.
Move beyond ad-hoc testing to structured, repeatable evaluation processes that scale with your AI deployments.
Use language models to evaluate language models — with proper calibration, bias-aware scoring, and domain-specific precision.
Connect evaluation results to deployment decisions. Build governance systems that prove your AI keeps working at scale.
90–95%
Subject matter experts close the gap. The 70% → 95% jump is where domain expertise lives.
Each stage of the eval engineering lifecycle is defined by measurable outcomes. Click through the cards to explore how accuracy, cost, and coverage evolve as your system matures.
This book takes an open, vendor-neutral approach to the discipline of evaluating AI systems. It covers the full lifecycle from initial testing through production monitoring and continuous improvement.
Open collaboration and transparency of methods will lead to greater progress towards trustworthy AI. We believe that evaluation engineering has reached a critical inflection point where it will define the next generation of production AI.
Key concepts, frameworks, and best practices — all in one page.
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