How to Test Your AI Prompts: A Simple Evals Guide for Teams

If a prompt powers something important, such as a support reply, a summariser or a classifier, you need to know whether it works and whether a change makes it better or worse. That's what evaluations ("evals") are for.

• AI output varies from run to run.
• A tweak that fixes one case can break others.
• Model updates can change behaviour.
• Anecdotes hide failure rates.

1. Collect 30 to 100 real examples that reflect typical and tricky inputs.
2. Include edge cases: missing information, rude customers, ambiguous requests, long inputs.
3. Define the expected outcome or criteria for each, written down.
4. Keep a hold-out set you don't tune against, to check you haven't overfitted.

• Exact checks: correct category, valid format, required fields present.
• Rules and tests: length limits, banned phrases, correct calculations.
• Human review: a rubric for tone, accuracy and helpfulness, scored blind if possible.
• AI-as-judge: a model grades outputs against criteria. Useful at scale, but spot-check it against human judgement.

1. Run the current prompt on the test set and record scores.
2. Change one thing (prompt, model or settings).
3. Re-run and compare.
4. Review failures to understand patterns.
5. Add new failure cases to the set.

• Accuracy or pass rate
• Severity of failures (a rude reply is worse than a slightly long one)
• Cost and latency
• Consistency across repeated runs

Sample live outputs weekly, track user feedback, and re-run evals whenever the model, prompt or data changes.

A small, well-chosen test set turns prompt tweaking from guesswork into engineering. Start with 30 examples and a clear rubric.