A useful question to ask of every AI tool
What does this tool make easier to verify? A practical starting point for evaluating AI in a workflow, beyond the quality of its first answer.
In this entry
A fluent answer is a compelling interface. It can also make it easy to move past the question of how the answer will be checked.
This example entry proposes an evaluation question rather than reporting results from a particular tool: what does this make easier to verify?
Define the task before the demonstration
Choose a task you understand well enough to evaluate. Describe the input, the intended result, and the failure modes that would matter.
For a research summary, a useful check might be whether every important claim leads to a source that supports it. For a code change, it might be whether a reviewer can understand the change and run a meaningful test.
Look at the whole workflow
The first output is one part of the work. Reviewing, correcting, and integrating it are other parts.
An evaluation should consider that complete sequence. If you measure it, record the method, the version of the tool, the date, and the limitations of the comparison.
Make disagreement cheap
A useful tool should leave room for the person using it to question its output. References, small changes, visible assumptions, and reproducible steps all help.
This does not settle whether a given tool belongs in a workflow. It gives the decision a more concrete starting point than a polished demonstration alone.