Technology
Evaluation Before Automation: Testing AI in Context
An AI system can perform well in a demonstration and still fail in the work around it. Evaluation becomes useful when it reflects the task, the consequences of error, and the people who must rely on the result.
Begin with a decision
Before choosing a metric, identify the decision the system is meant to support. A drafting assistant, a search tool, and an automated action need different tests because their errors create different consequences. The evaluation should describe the user, the input, the expected outcome, and what happens when the system is wrong.
Use more than one kind of evidence
Accuracy is useful, but it is rarely sufficient. A balanced evaluation can include task completion, factual support, robustness to unusual inputs, privacy, security, consistency, latency, and the amount of human correction required. Qualitative review matters when a score cannot capture whether an answer is genuinely useful.
Test the complete workflow
A model does not operate alone. Retrieval, permissions, prompts, tools, interfaces, and escalation rules all affect the result. Test ordinary cases, edge cases, and deliberately difficult cases. Observe how real users interpret the output and whether the interface encourages an inappropriate level of trust.
Keep evaluating after launch
Inputs change, usage changes, and system components are updated. Teams therefore need a repeatable set of test cases, clear thresholds, incident review, and a way to compare new versions with the current one. Monitoring should lead to decisions: investigate, restrict, improve, or pause.
NIST describes trustworthy AI evaluation as context-dependent and broader than a single measure. The practical lesson is simple: decide what good means before automation makes the decision harder to inspect.