An AI feature can look impressive in a few demos and still fail on everyday work. Evaluation should reflect the task people will perform, the range of input they provide and the mistakes they cannot accept.
Write down what success means
Describe the user task and the standard for a useful result. A support drafting tool may need accurate facts, the right tone and an editable response. A categorization tool may need consistent labels and a clear way to flag uncertain cases.
Build a varied test set
Include short and long inputs, missing information, conflicting instructions and examples from different user groups. Keep sensitive test data protected. Separate routine cases from edge cases so average performance does not hide serious failures.
- Define error categories before reviewing results.
- Include cases that should receive no answer.
- Retest after changes to prompts, data or models.
Use human review deliberately
For tasks where judgment matters, ask reviewers to score outputs against a shared rubric. Compare disagreements and refine the rubric. Automated checks can catch format or obvious factual mismatches, but they may miss whether an answer is genuinely useful.
Monitor the live workflow
Track correction rates, escalations and user feedback after release. Give people a clear way to report harmful or confusing outputs. Reevaluate when the feature expands to new tasks or data sources.
Practical next step
Test the feature against real work and known failure modes. Keep evaluation active after launch because the context around an AI feature changes.
Explore BS InfoTech services or tell us about your project.