The decision
A small evaluation set is a repeatable collection of inputs and expected behavior. It makes prompt changes comparable and exposes regressions that a polished example hides. Start with real permissioned or synthetic examples that resemble the task, then add difficult cases that exercise your limits.
A worked example
An update writer’s first set contains ten routine notes, two empty notes, two contradictory dates, two long notes, and two instruction-override attempts. Each example has a factual support rule and a clear expected failure or review state. A writing-style score is recorded separately from correctness.
How to put it into practice
- Give each example a stable ID and record the expected behavior before testing.
- Separate representative cases from edge cases so one average does not hide a severe failure.
- Store prompt version, model configuration, latency, cost, and result for each run.
- Keep a held-out set for later comparison instead of tuning against every example indefinitely.
A failure to plan for
An evaluation set can become unrepresentative as customers change. Add new failures from production after removing private details and obtaining permission where needed. Keep the original set for regression comparison.
Try it on your project
Create fifteen cases for one feature. Define pass/fail rules that another person could apply. Run the same configuration twice and record variability rather than assuming a single run is decisive.
Keep the next step small
Use the free demand scorecard or planning tools to make your assumptions explicit. The $19 launch kit brings the blueprint and seven editable worksheets together.