A set of techniques and datasets to evaluate the quality, safety, and capability of AI models on specific tasks.
Detailed explanation
Evals (evaluations) are the process of measuring how well an AI model performs on a set of tasks. There are public evals (like SWE-Bench, MMLU, HumanEval) and custom evals built for a specific product. The problem with public evals is that they can be contaminated by training data, have poorly defined tasks, or not reflect the real use case. OpenAI separated signal from noise in coding evals and found 30% of tasks broken. The recommendation for product managers: build your own evaluation dataset with real product cases, measure accuracy on your specific task, monitor regressions in production, and use public benchmarks only as a directional reference.
How to use it in product decisions
Start evals with the decision that will be made from the result. Collect real examples, define what counts as success, and assign greater weight to high-impact failures. Separate development and test sets so the product is not optimized against its own exam. Combine automated evaluation with calibrated human review and track evaluator disagreement. Version the dataset, prompt, and model. The useful metric is not one score but the distribution of quality, risk, and cost across cases that represent production use.