Test an AI workflow on work you can assess

Prepare the answer before testing the tool

Choose a narrow task and a small public or fictional record. Independently list the events, passages, or distinctions that a correct result must preserve. Keep this answer set separate from the material given to the system.

An evaluation needs defined inputs and success criteria. Because model outputs can vary, repeated trials can reveal reliability that a single demonstration conceals. The final result matters as well as the system's account of what it did. [source]

Define errors separately

For an agent, inspect the execution record as well as the final prose. Count missing source IDs, failed reads omitted from the report, unauthorized attempted or executed calls, inappropriate retries, and premature completion claims. Keep attempted and executed actions separate. A rejected call shows a boundary worked for that call; a polite final answer does not show whether earlier actions were authorized. Apply these checks to the bounded inventory trace.

For a chronology, count missing relevant events, unsupported added events, wrong dates, wrong source locations, and lost qualifications. Classify their materiality. A wrong date central to the dispute may matter more than several harmless formatting defects.

For an extraction task, precision asks how many returned items were correct. Recall asks how many expected items were found. In a fictional test with ten expected events, a system returns eight rows: six correct and two unsupported. Precision is 6/8, or 75%; recall is 6/10, or 60%. These simple counts need clear matching rules.

If two descriptions reasonably represent the same event, decide how to score them before comparing systems. Otherwise your grading can change with the output you prefer.

Include hard cases intentionally

Use a proposed event, a conditional statement, a duplicate email, an ambiguous date, a missing attachment, contradictory sources, and an unanswerable question. Label what each case tests. A small deliberate set can teach more than a large set of straightforward examples.

Retain a fresh set for later checking. If every prompt revision is optimized against the same examples, improvement may reflect memorizing those examples rather than a more reliable method.

Measure the completed job

Record preparation time, generation time, review time, corrections, and export effort. Note how easy it was to identify a mistake. An output with slightly lower initial accuracy may still be useful if the errors are transparent and inexpensive to correct; hidden material omissions can be much more costly.

Do not collapse all of that into one unexplained score. Keep material error categories visible, particularly where a minimum requirement must be met before use.

Retest when something material changes

A new model, parser, prompt, retrieval index, document type, or permission arrangement can alter performance. Save the inputs and configuration with each run. Use failures as specific regression cases and review whether the test still represents actual work. Passing a bounded evaluation supports a bounded conclusion about that workflow.