When AI Learns How To Pass The Test, What Are We Actually Testing?

Margarita Simonova is the founder of ILoveMyQA.com.

In QA, we are used to a fairly simple idea: Define what should happen, test it and see whether the product behaves the way we expect. AI makes that relationship less straightforward. We can evaluate an AI system against the same benchmarks, test sets and evaluate scoring criteria again and again. Then we change the model, the prompts or the surrounding logic until the results improve.

At first glance, that sounds exactly like what we want. But there is another possibility: The system may simply be getting better at satisfying the way we measure it. A better score does not always mean a better product.

Passing The Test Is Not The Same As Solving The Problem

We already see versions of this in traditional QA. If a team is measured heavily on automation coverage, automation coverage usually goes up. If success means running more tests, teams get very good at running more tests. None of that guarantees that customers are seeing fewer problems. AI adds another layer because the behavior we measure can become the behavior we optimize.

Take an AI customer support system. Suppose the company measures it mainly by resolution rate and how quickly conversations are closed. The numbers start improving, and from a dashboard perspective, everything looks great. But then you look closer and find that some customers are getting quick answers that do not actually solve their problems. The conversation was technically “resolved,” but the customer still had to contact support again.

The AI performed well against the metric. It did not perform well for the customer. That is the kind of gap QA needs to look for.

What Did We Actually Ask The System To Optimize?

In each case, the metric can improve while the product moves in the wrong direction. That is why QA cannot only ask whether the system met the target. We also need to ask whether we picked the right target in the first place.