AI Pilot Evaluation: Build a Useful Test Dataset

Recueillir des informations réalistes.

Sélectionnez des exemples qui reflètent les apports habituels de la tâche et les cas difficiles. Incluez des informations incomplètes et des demandes hors de la portée prévue. Vérifiez que vous avez l'autorisation d'utiliser le matériel et supprimez les données personnelles inutiles. Un ensemble pratique d’exemples clairs peut masquer les conditions de fonctionnement importantes.

Décrire les réponses acceptables.

Notez ce qu'un résultat utile doit contenir et ce qui le rendrait inacceptable. Certaines tâches nécessitent une valeur correcte ; d'autres ont besoin d'une explication appuyée ou d'une demande claire d'informations complémentaires. Demandez à la personne responsable du flux de travail de revoir ces attentes avant d'évaluer les résultats.

Enregistrer les échecs comme matériel d'apprentissage.

Conservez ensemble les décisions d'entrée, de sortie et de révision lorsqu'un exemple échoue. Recherchez des modèles plutôt que de vous fier à une seule impression globale. Utilisez les résultats pour ajuster les limites des tâches ou le processus de révision, et vérifiez ces modifications par rapport à l'ensemble d'exemples plus large avant d'étendre le projet pilote.

Build an evaluation set with traceable judgments

Describe the intended task and collect examples from the conditions in which it will operate. Use approved, appropriately protected material or carefully constructed fictional examples. Include common inputs, uncommon but important cases and requests outside the task boundary. Record why each example belongs in the set.

Write an expected result or a review rubric before testing. Have reviewers compare a small selection and resolve disagreements about correctness. Keep development examples separate from the evaluation used to make a continuation decision. When the prompt, model or source material changes, record the version and rerun relevant checks. Preserve individual failures instead of reporting only an overall score that can conceal a serious error.

An evaluation case and result log

A spreadsheet can be enough for an early pilot if permissions and versioning are appropriate. Use stable identifiers so a result can be traced to the original case.

• Case ID and category: common task, ambiguous input, exceptional case or out-of-scope request.
• Input and context: approved material, relevant source version and information available to the system.
• Expected behaviour: required facts, permitted uncertainty and outcomes that must be rejected.
• Review rubric: dimensions such as factual accuracy, completeness and suitability for the task.
• Observed output: system version, result, reviewer judgment and explanation of any disagreement.
• Disposition: correction, additional investigation, acceptance or a reason to stop the pilot.

Do not let the demonstration become the test

If the team repeatedly adjusts a system against the same examples, those examples no longer provide an independent view of performance. Keep a separate evaluation set and describe its limits.

Avoid forcing subjective outputs into a single unexplained pass/fail score. Review disagreement and significant failure categories separately. Do not treat an absent error in a small sample as proof that the error cannot occur. The final decision should reflect both the evidence and the consequences of getting a result wrong.

How many evaluation examples are enough?

There is no universal number for every task. Begin with coverage of meaningful input types and failure cases, then expand where results are uncertain. Consider the variety of work and consequences of errors rather than choosing a sample size solely because it is convenient.

ILLUSTRATIVE PLANNING EXAMPLE

Example: evaluating document summaries

Choose documents representing the lengths, structures and language the team expects to handle. Include a document with conflicting dates and one with an important exception. Write down the facts a useful summary must preserve and the errors that make it unacceptable. Remove confidential details before sharing examples outside the approved environment.

Keep a separate evaluation set and record reviewer judgments consistently. A few impressive summaries are not enough to establish suitability for the full workflow.

FURTHER READING

Official resources to explore

Explore the related official guidance below. The templates and examples are practical starting points to adapt to your own project.

• NIST AI Risk Management Framework Playbook