Give AI a smaller, better problem.

A broad request can produce an impressive-looking answer that is difficult to evaluate. A small, well-defined task makes it easier to see whether an AI tool has actually helped.

Instead of starting with an entire workflow, choose one step with a clear input and an observable result.

Define what a useful answer looks like

For a documentation task, that might mean a concise explanation that matches the code. For a coding task, it might mean a change that satisfies a specific behavior without breaking an existing one.

Write down the constraints before asking for the output. Include relevant examples and identify the sources that should take priority.

Build in a way to check

Review is easier when the desired result is concrete. A small test case, a comparison with the original source, or a checklist of required fields can expose gaps that fluent prose hides.

Ask what evidence would make the result trustworthy. Then use that evidence instead of relying on the confidence of the answer.

Keep decisions visible

When an experiment works, record the task, the context provided, and the checks used. When it fails, record what went wrong.

That creates something more useful than a collection of successful prompts: a picture of where the tool fits, where it needs supervision, and where a different approach would be simpler.

A useful first experiment does not need to be ambitious. It needs to be understandable enough to learn from.