THE PRACTICAL TAKEAWAY
Save representative inputs, define what a good answer must contain, and rerun the same cases whenever the system changes.
Keep examples, not impressions
When an AI result looks good, it is tempting to declare the system ready. The next input may be less forgiving. A small test set gives your team something more repeatable than a memory of the best demo.
Begin with examples from the actual task, using approved or safely constructed data. Include straightforward cases, common edge cases, and situations where information is absent. OpenAI’s evaluation guidance emphasises tests specific to the task rather than relying on a general impression of quality.
Describe what good means
For each example, write the key facts the answer must preserve, errors it must avoid, and behaviour you expect when uncertain. Separate correctness from style. A clear, friendly answer can still be wrong.
Use a simple scoring rule that another reviewer can understand. For a summary, check whether required facts are present, whether any unsupported facts were added, and whether the result is usable after review. Keep the original input beside the output.
Use the set when something changes
Rerun the examples after changing the prompt, source material, model, or tool connection. Compare failures as well as average scores. Keep a few examples out of routine prompt tuning so you have a less rehearsed check.
Add new failure cases as they occur, while removing duplicates that contribute little. A small set does not prove a system safe in every situation; it makes regressions easier to spot. Its value grows when it reflects the work your team actually encounters.
Source & further reading
OpenAI: Evaluation best practices
Background on task-specific evaluation. The lightweight business checklist below is our editorial recommendation. Reviewed 5 October 2026.
ModelMillionaire publishes AI-assisted editorial guidance. Examples are illustrative unless explicitly identified as documented cases. Our editorial approach.