How to evaluate an AI tool with a real task

Use a repeatable evaluation to judge usefulness, accuracy and fit before moving an AI tool into everyday work.

Define a good result

Choose a task you already understand and write down required facts, format, constraints and unacceptable mistakes. For writing, specify the audience and facts. For code, define expected behaviour and appropriate tests. Decide what would make the output unusable before looking at a polished answer.

Use representative inputs

Prepare ordinary examples and a difficult case. Remove confidential information unless you have permission and have assessed the vendor’s data terms. Keep comparable instructions and original inputs so you can revisit the result.

Inspect the evidence

Open citations and check factual claims against the source. Inspect code changes and run tests. Examine media at the size and format in which it will be used. A confident tone or attractive layout does not establish correctness.

Include a revision

Ask for a realistic change and see whether the tool preserves what was already correct. Record the total effort needed for a usable result. A fast first draft may offer little value if every correction introduces another problem.

Decide where it fits

Review collaboration, accessibility, exports and integrations. Document the use case, plan, date, strengths and unresolved issues. Decide where supervision remains necessary and revisit the decision when the product changes. Our research methodology explains how a test differs from a directory profile.