An AI evaluation dataset is useful when it helps a team make a defined decision. Before collecting prompts or choosing a scoring tool, state what change is being reviewed and which failures would make that change unacceptable. A benchmark score alone does not establish that a workflow is ready for its users.
Start with the decision, not the metric
Write the decision in operational terms: compare two prompt versions, review a retrieval change, or decide whether a workflow should escalate a response to a person. Identify who owns that decision. Different decisions can require different examples and review criteria; avoid combining them into a score that nobody can interpret.
Keep a small evidence contract for each example
Record the task, permitted input, relevant context, expected behavior, rubric version and provenance. Expected behavior may be an acceptable answer boundary rather than a single exact sentence. For a retrieval task, retain the authorized source evidence needed to distinguish a missing source from an unsupported generated answer. Minimize personal or confidential data rather than copying production records by default.
Separate failure slices from the overall average
Group examples by a meaningful risk or behavior: missing context, conflicting sources, unsupported requests or required escalation. Inspect the slices separately. An improvement across common requests can conceal a regression in a less frequent but important case. Slice definitions are a review aid, not proof that all real-world failures are covered.
Hold out examples from tuning
Examples repeatedly used to adjust prompts or workflow logic remain useful regression tests, but they no longer provide independent evidence of generalization. Maintain a held-out set for the decision being tested and document when examples move between sets. Protect test access where leakage would undermine the comparison.