Evaluate an AI workflow against the work it is supposed to complete. A fluent answer can still be wrong, incomplete, or operationally unusable.
Start with a written acceptance standard and representative cases. Keep output quality separate from the cost and effort required to use the system.
Define the case record
For each case, record the task, input context, expected characteristics, known complications, and the basis for judging the result. Where there is no single correct answer, describe the important requirements and unacceptable failure modes.
Use information you are authorized to process. Remove unnecessary sensitive details while preserving the characteristics that make the case realistic.
Apply a practical rubric
| Dimension | Reviewer question |
|---|---|
| Factual accuracy | Are important claims supported by the available evidence? |
| Completeness | Does the output include what the user needs to finish the job? |
| Uncertainty | Does it recognize missing or conflicting information? |
| Action appropriateness | Is the proposed action permitted and justified? |
| Usability | How much human correction is required? |
| Recovery | Does the workflow handle tool failures and exceptions clearly? |
Choose a simple scoring scale with examples of each level. Record serious failure categories separately so an average does not conceal them.
Build a representative set
Include ordinary work, unusual but legitimate inputs, incomplete information, and previously observed failures. Document which kinds of work are not represented yet.
Do not claim a general success rate from a small hand-picked collection. The set’s composition determines what the result can support.
Keep some cases separate from the examples used to develop the workflow. Otherwise the team may learn how to pass the test without improving broader usefulness.
Compare versions fairly
Run the same cases with the relevant model, prompt, tool, and configuration versions recorded. Have reviewers apply the same criteria and inspect disagreements.
Measure correction time and operational cost alongside quality where those affect the decision. A higher score may justify more expense for one task and be unnecessary for another.
Turn the review into a release decision
Write what improved, what regressed, which failures remain, and what level of responsibility the evidence supports. A workflow may be ready to draft recommendations while still being unsuitable for executing them automatically.
The NIST AI Risk Management Framework provides broader context for measuring and managing AI risk. This rubric is an operating template, not a certification standard.
Continue sampling real work after launch. Evaluation is valuable when it influences deployment, permissions, and maintenance decisions, not when it produces a score that the team never uses again.
Co-founder and CEO of Stackmatix, startup advisor, and former Head of Sales at MightyHive. Connect with Matt on LinkedIn ↗ · More about Matt →