Evaluate an AI Capability with a Small, Repeatable Test

Worked example and checks

A practical evaluation method for claims about document understanding, code generation, and automation.

Turn a claim into a question

Instead of asking whether a tool is intelligent, choose a task with a checkable answer. For document extraction, prepare a short table with known values. For summarization, write a note containing three explicit actions and one unresolved question. Keep the original as your reference.

Separate extraction from interpretation

A tool may locate text without understanding its meaning. Keyword search, sentence selection, OCR, and language-model generation are different operations. Ask what method produced the result. A highlighted passage does not establish that the surrounding conclusion is true.

Run more than one example

Try a clear case, a missing-value case, and a case with an exception. Keep instructions and output requirements consistent. Record errors and corrections, including plausible-looking results that contradict the source. Avoid choosing only the example that makes the tool look successful.

Consider the operating conditions

Measure preparation, waiting, review, and correction effort. Check file limits, privacy controls, and available export formats in official documentation. A demonstration does not establish reliability for every workload, and a model benchmark does not describe your complete workflow. Use the trial to decide where human review remains necessary rather than treating a broad claim about AI as a product guarantee.

Reference and corrections

Official documentation for this topic. Check the version and platform covered by the source. Guidance revised 22 October 2026.

Report an incorrect instruction through our contact page. Include the app, operating system, command, and observed result.