Evals

Evals (evaluations) are systematic tests designed to measure and assess how well an AI model performs on specific tasks and objectives. They are critical components of AI development and deployment, helping teams understand model capabilities, limitations, and areas for improvement. Evals can measure various dimensions of model performance, including accuracy (how often the model produces correct answers), reasoning quality (whether the model's logic is sound), tone and style consistency, safety (whether outputs avoid harmful content), and factual correctness. Different types of evals serve different purposes: automated evals use metrics and scoring functions to quickly assess large numbers of outputs, while human evals involve expert reviewers evaluating model responses for nuance and quality that automated systems might miss. The process of creating effective evals involves defining clear success criteria, selecting representative test cases, and establishing benchmarks for comparison. Well-designed evals help teams identify when models are ready for deployment, detect performance regressions, and guide improvements through fine-tuning or prompt engineering. As AI systems become more complex and are deployed in higher-stakes applications, comprehensive evaluation frameworks have become essential for ensuring reliability, safety, and user satisfaction. Regular evaluation throughout the model lifecycle helps maintain quality standards and builds confidence in AI system performance.