Designing Evaluations That Actually Tell You Something

A good AI evaluation is supposed to answer a business question, not just spit out a number and call it a day. That’s the core idea here: start from the workflows the system is meant to support, then build the test around the places where performance actually changes a decision.

That means focusing on the tasks that carry real weight — the high-volume stuff, the customer-critical flows, the capabilities a team is thinking about shipping, and the areas where mistakes would hurt. The article argues against treating every task equally. A support assistant’s everyday cases, a risky workflow, and a capability that’s still being considered for launch do not deserve the same amount of attention.

The dataset itself should be structured enough that the same case can be run across models, prompts, or system versions and compared fairly. A single example can include the request, context, tools, expected outcome, a reference answer if there is one, grading criteria, and metadata like task type or risk level. For simple work, that might stay tiny. For agentic systems, it can look more like a simulated environment with actions and a final-state check.

Representativeness matters, but not in the dumb “copy production exactly” sense. A rare case with serious legal, financial, or reputational risk may deserve more weight than its frequency would suggest. The point is coverage: different difficulty levels, relevant customer segments, known failure modes, and enough of the right kinds of examples that the results reflect the system you actually care about.

The article also pushes back hard on happy-path-only testing. Easy examples tell you very little about messy reality, where users leave out context, contradict themselves, or ask for something ambiguous. That is where models start to separate. For enterprise systems, the important failures are often the ones that should trigger a clarification, a refusal, an escalation, or an honest admission of uncertainty.

And once the eval is running, the work is not just about scoring. Human graders need calibration, agreement needs to be checked, and quality control has to match the stakes. A bad eval process can be almost as misleading as a bad model. If the failure is severe, it should be weighted like a severe failure, not treated like a typo. The best source of new test cases is production failure itself, which is about the most honest feedback loop in the whole piece.