Test, don't admire

A successful AI answer can feel remarkably convincing. That is precisely its risk: when it is fluent, fast and plausible, it is easy to mistake a strong first impression for reliability. Anyone who wants to work with AI needs less admiration and more small, repeatable tests.

Not every correct answer is a good test

One example tells you almost nothing about the quality of a workflow. Perhaps the task was unusually easy. Perhaps the available material happened to fit perfectly. Perhaps the answer sounds convincing while a decisive point is missing. A test begins only when you define in advance what counts as usable.

This is not an invitation to distrust for its own sake. It is a form of care. You are not testing whether a system is magical; you are testing whether it helps you usefully under clear conditions.

Three scenarios reveal what actually happens

The positive scenario describes the normal case: a clear task, suitable material and a result in an agreed format. Here you check whether the work is genuinely made easier. A good result is not simply long or elegant; it serves the agreed purpose.

The negative scenario sets a boundary. What should happen when the basis is missing, the request is unclear or a claim cannot be supported? A useful system may ask a question, mark uncertainty or refuse at that point. That is not a failure; it is often evidence that the boundary is visible.

The repairable scenario goes one step further. You intentionally supply a small error, a conflicting instruction or incomplete material. Then you test not only whether the problem is noticed, but whether the path to correction is clear: What is missing? What changed? What must a person decide?

Acceptance criteria make quality visible

An acceptance criterion is a short, observable statement. For example: “The response only names claims found in the supplied material.” Or: “When information is missing, it asks a question first.” These statements matter because two people can check them without arguing about a vague impression.

Keep criteria small. Three to five clear points are more useful than a long wish list. Be equally clear about what is not being tested. A test for a structured summary does not need to judge creativity, tone, research and legal interpretation at the same time.

An error log is not failure

When a test fails, the first question is not, “Why is the AI bad?” Ask instead: Was the task clear? Was the material sufficient? Was the rule understandable? Could the criterion actually be checked? Often the most useful insight is not in the answer but in an unclear hand-off.