AI Evals

AI Evals are structured, systematic frameworks for rigorously measuring the performance, accuracy, and reliability of AI models, especially LLMs, against explicit business and safety objectives.

AI Evals operationalize reliability: they transform probabilistic LLM outputs into auditable, quantifiable metrics. This process moves beyond simple unit tests, using hybrid scoring systems—combining automated metrics, human-in-the-loop review, and LLM-as-a-judge grading—to validate performance. Key evaluations include pre-deployment benchmarking (e.g., MMLU, HumanEval), continuous post-deployment monitoring, and complex scenario simulation to test for factual correctness, contextual alignment, and safety/bias quantification. The goal is clear: enforce governance, inform fine-tuning, and gate deployment for trusted, enterprise-grade AI applications.

https://github.com/openai/evals