Designing Evaluations That Actually Tell You Something

surgehq.ai · 1 month ago · 4

The article explains how to design AI evaluations so they support real business decisions by selecting the tasks that matter and structuring datasets with clear inputs, tools, outcomes, and grading criteria. A useful evaluation should focus on workflows that matter most to the business rather than evaluating every possible task equally. It emphasizes adding messy, edge, and adversarial cases and using grader calibration, inter-rater agreement, quality control, and failure weighting to improve trust and prevent overfitting to the eval set.