How Do You Know Your AI Works? A Plain Guide to Evals

With ordinary software, a test either passes or fails, and you move on. AI is different. The same question can get a great answer today and a subtly wrong one tomorrow, and “good” is not a yes or no, it is a rate. So the honest question is not “does it work?” but “how often, and how do we know?”

That is what evals are for. They turn AI quality from a vibe into a number you can measure, defend, and improve.

Start with what good looks like

Before you build anything, decide what a correct answer actually is. Take real examples from the work and write down the right response for each, the ground truth. This sounds obvious, but most teams skip it and end up arguing about quality with no shared definition. If you cannot say what good looks like on ten real cases, you are not ready to measure, or to ship.

Build a golden set

Now collect those examples into a small, representative golden set: real inputs paired with their known-good answers, covering the normal cases and the tricky edges. It does not need to be huge. A few dozen well-chosen cases give you a repeatable yardstick you can run the AI against any time. This set becomes your source of truth, the thing every version of the system has to face.

How to score it

There are three honest ways to score answers, and good evals usually use a mix.

• Exact-match for structured tasks, where there is one right value: data extraction, classification, a pulled-out date or amount. Fast, cheap, and fully automatable.
• Human review for judgment calls, where tone, nuance, or safety matter. It is the most trustworthy and it sets the standard, but it is slow and does not scale on its own.
• LLM-as-judge to scale, using one model to score another’s answers against your criteria. It runs cheaply on every change, so you keep tight feedback, as long as you keep human spot checks to make sure the judge stays honest.

The point is to match the method to the task, not to chase a single accuracy number that hides what is really happening.

From vibe to number

Real questions Tricky edge cases Known-good answers

The eval run

Every version faces the same yardstick

A score you can defend

Watch it after launch

Evals are not a launch gate you pass once. Real-world inputs shift, and the model underneath you can change, so quality can slip without anyone touching the code. This is model drift, and it is invisible until you measure it.

So keep scoring in production, and re-run your golden set on every change as a regression check. When a number drops, you find out from a dashboard, not from an angry user. That is the difference between managing quality and hoping for it.

Tie evals to the metric that matters

One trap is to fall in love with an abstract accuracy percentage that does not mean anything to the business. The strongest evals connect to the real number from your proof of value: time saved, error rate, response time, conversion. An eval score is only useful if a higher score reliably moves that metric. Measure the thing that actually changes the outcome, not the thing that is easy to chart.

You cannot trust what you cannot see

Evals are how AI grows up from a clever demo into a system you can rely on. They let you say, with evidence, how good it is, prove it is not getting worse, and improve it on purpose. You cannot improve what you cannot measure, and you cannot trust what you cannot see.

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Not sure how good your AI actually is? That is exactly what evals answer. Book a free consult and we will define what good looks like for one workflow, build a golden set, and give you a quality number you can stand behind.