Build a Representative AI Workflow Evaluation Dataset

A versioned test set with acceptance criteria for a practical business AI workflow, with steps, an acceptance test, and a reusable prompt.

By Dr. Connor Robertson · Published October 24, 2026 · Implementation playbook

Deliverable: A versioned test set with acceptance criteria. Use this playbook when your team needs a repeatable operating decision with evidence that another person can inspect.

The business situation

A service intake classifier used across several customer request types. This is an illustrative scenario, not a reported client result. Start with a small approved sample and the actual constraints of your team.

The operating approach

Define success before selecting examples. Include routine cases, ambiguous requests, missing data, and cases that must be escalated. Separate development examples from held-out cases used to decide whether to release.

Build it step by step

  1. Sample completed tasks across categories and time periods.
  2. Remove unnecessary personal data and retain the needed context.
  3. Have qualified reviewers label expected output and unacceptable errors.
  4. Version the set and reserve cases that prompt authors do not tune against.

A concrete acceptance test

Add a request containing both a billing complaint and a service outage. Decide in advance whether it needs multiple labels or escalation, then assess the classifier consistently.

Record the input, expected behavior, actual result, reviewer, and version tested. Repeat this case after a prompt, source, model, or integration change. Include the cases that failed in the next regression run.

The mistake to avoid

A test set containing only clean examples exaggerates performance. Report results by case type and severity.

A reusable working prompt

Design an evaluation dataset specification for this workflow. Include sampling, labels, edge cases, reviewer disagreement, held-out cases, versioning, and release criteria.

Replace general descriptions with your approved process details. The prompt helps prepare an artifact; the process owner verifies the finished result before it changes a live workflow.

What to keep in the handoff

Save the accepted a versioned test set with acceptance criteria, its source versions, test evidence, unresolved questions, and a named owner. Set a review trigger tied to a meaningful business change, rather than leaving the pilot without maintenance.

Continue implementation

Explore AI Knowledge Bases and Evaluation or contact Dr. Connor Robertson about your business workflow.