How to evaluate an AI automation before trusting it

01

Write the claim you intend to test

“The AI works” cannot be evaluated. A testable claim identifies the task, population, quality threshold, and operating condition. For example: the system extracts five required fields from the current supplier forms and lets a reviewer verify them faster than manual entry.

List claims the test will not support. A document extraction result does not prove autonomous approval is safe. Performance on historical email does not prove every future language or customer segment is covered. Explicit limits prevent a narrow score from becoming a broad marketing statement.

02

Build a representative set

Sample from the work the system will actually encounter. Include common categories in realistic proportions, then deliberately add rare but consequential cases. Preserve examples with missing information, conflicting instructions, unusual layouts, and cases that should be rejected.

Keep a held-out portion that developers do not use for routine prompt tuning. If every failure becomes a prompt example and then remains in the reported score, the evaluation gradually measures memory of the test rather than general performance.

03

Use a task-specific rubric

Define what correct, acceptable, and unacceptable mean for each output dimension. A summary may be judged on factual support, required coverage, prohibited content, clarity, and format. An extraction task may use exact values, normalized values, missing-field handling, and source references.

When human judgment is required, give reviewers examples and resolve disagreements. Measure agreement on a subset. If knowledgeable reviewers cannot apply the rubric consistently, the success criterion needs clarification before the model is blamed.

• Task correctness and completeness
• Support from the supplied source material
• Correct escalation when information is missing
• Format and downstream validation
• Operational time, cost, and reviewer effort

04

Measure errors by consequence

Average accuracy can hide a dangerous error class. Separate false acceptance from false escalation, unsupported claims from harmless formatting problems, and mistakes on critical fields from mistakes on optional metadata. Weight decisions according to the workflow consequence.

Record examples for each category and inspect patterns. A cluster may point to incomplete retrieval, ambiguous policy, poor OCR, a schema issue, or a missing deterministic rule. Evaluation should guide engineering, not only produce a score.

05

Compare with the existing process

The relevant benchmark is often the current human and software workflow, including its errors, delays, and cost. Compare completed items, correction effort, elapsed time, and outcomes under similar conditions. Automation does not need to imitate every human step if it improves the controlled result.

Run a shadow or draft-only period when possible. The system processes real work without taking the final action, and reviewers compare its output with the normal path. This reveals operational issues that a static test set cannot capture.

06

Keep evaluation alive after launch

Version the evaluation set, rubric, prompts, models, retrieval data, and parsing code. Run the suite before releases and retain prior results. Add newly discovered failure patterns without letting the set become dominated by one recent incident.

Monitor real correction rates, escalations, input drift, latency, and cost. Periodically sample apparently successful outputs because silent quality problems may never enter the exception queue. Trust comes from ongoing visibility and a tested response when quality changes.