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.
Promptic launched an optimization platform aimed at GenAI applications that benchmarks models, tunes prompts and agents, and optimizes tool use against a user’s own data and business metrics.
REVERSAL-BENCH was introduced to benchmark reset-free reinforcement learning by varying environment reversibility with a parameter ρ and using a reset oracle to verify state recoverability across manipulation tasks in multiple physics engines. The benchmark spans eight manipulation settings in five physics engines. As ρ increases, reset-free agents become absorbed into irrecoverable states and stop learning while episodic agents continue, and the authors release the dataset and show a safety shield can predict recoverability but only recovers successfully when physical escape is possible.
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