# Testing an AI feature: what "good enough" looks like

_"Good enough" is not a cop-out, it is a number you have to define on purpose. The teams that ship well decide what quality bar the feature must clear, and for which cases, before they argue about whether it clears it._

Two anonymized stories, from an EV-charging hardware leader and a developer-tools founder, on defining a concrete quality bar for an AI feature, testing against it, and knowing which cases have to be perfect and which can be merely good.

# Testing an AI feature: what "good enough" looks like

"Good enough" sounds like an excuse until you try to write it down as a number. Then it becomes the hardest and most useful decision in the project. For a non-deterministic AI feature, you cannot promise perfection, so testing is really the act of setting a bar you can defend and checking whether the feature clears it, case by case.

## A bar you can name, checked with real checkpoints

A hardware product leader in EV charging built his whole company around a defined quality bar. He called it Apple quality, and he meant something specific, not a slogan. There are sensors inside every unit monitoring current and temperature, and a series of checkpoints the product has to pass. The bar is written into the hardware, so "good enough" is not a feeling anyone has in a meeting. It is a set of measurements the box either passes or does not.

That is the mindset an AI feature needs. Testing is not "does it seem good." It is "does it clear these named checks on these inputs," where the checks are concrete enough that two people would score the same output the same way. Because these features are non-deterministic, this is a genuinely different testing mindset than the pass-or-fail certainty of ordinary code. You are not asserting one correct string. You are checking dimensions like groundedness and relevance against a threshold you set on purpose.

## Good enough is different for different cases

The founder of a developer-tools company gave me the part people get wrong. Looking back at his earliest tutorials, he said they were genuinely rough, and yet they were still good enough at the time, because the alternative was nothing and the audience was small. The bar was set correctly for that context. What held him back most was a standard he could not yet meet, so he kept practicing until the work caught up.

The lesson is that "good enough" is not one global number. It is per case, per stakes. Some outputs of your feature can be merely good, because a human will review them or the cost of a miss is low. Others have to be near perfect, because a wrong answer is expensive or unrecoverable. A test suite that holds every case to the same bar either blocks a launch over low-stakes imperfection or waves through a high-stakes failure. You have to grade the cases first, then set a bar for each grade.

## How to make the bar concrete

Turning "good enough" from a feeling into a test looks like this:

- **Write the bar as checkable dimensions,** not "accurate," so scores are repeatable.
- **Grade your cases by stakes,** and set a higher bar for the outputs that hurt when wrong.
- **Decide the passing threshold before you run,** so you are not moving the goalposts to fit the result.
- **Put the near-perfect cases behind a human or a hard rule,** and let the rest clear a lower bar.

## How we approach it at Density Labs

In the AI Readiness Assessment, our $2,500 front door, we help you turn "good enough" into a written bar before you argue about whether the feature meets it. We separate the cases that must be near perfect from the ones that can be merely good, set a threshold for each, and define the checks that decide it. That conversation is far cheaper before launch than during a debate about whether to hold the release.

Good enough is not the absence of a standard. It is a standard you were disciplined enough to write down.