How to Design AI Evals: A Practical Guide

To design an AI eval, you write a repeatable test that scores your AI's output against what good looks like. You define the task, gather real examples, set clear pass criteria, and run the test every time the model or prompt changes. A good eval turns "it feels better" into a number you can trust.

Most teams building AI features skip this. They prompt, eyeball a few outputs, and ship. Then a model update or a reworded prompt quietly breaks something, and nobody notices until a user does. Evals catch that before it reaches production.

Start with the decision the eval informs

An eval is only useful if it changes what you do. Before you write a single test, name the decision it supports: ship or don't ship, prompt A or prompt B, this model or the cheaper one. If you can't name the decision, you're collecting numbers for their own sake.

That's the line between a vanity metric and an eval. A vanity metric says the AI "scored 87." An eval says "version 2 catches 12 percent more of the errors that get us support tickets, so ship it."

Step 1: Write down what "good" means

The hardest part of evals is not the tooling. It's defining quality so two people would grade the same output the same way. "Helpful" and "accurate" feel obvious until you try to score them.

Write a short rubric. For a support-reply feature, good might mean: answers the actual question, cites the right policy, and stays under 150 words. Be specific enough that a teammate could grade outputs without asking what you meant. If you can't write the rubric, you don't yet understand the feature well enough to ship it.

Step 2: Build a dataset from real inputs

Evals run against examples. The fastest way to make them useless is to invent clean test cases that never occur in the wild. Pull real inputs instead: actual user messages, real documents, the weird edge cases from your logs. Twenty real examples beat two hundred imagined ones.

Include the cases you're afraid of: the empty input, the hostile user, the question just outside the AI's scope. Those break in production, so those are what your eval has to cover.

Step 3: Choose how you'll score

There are three ways to grade AI output, and most real eval suites use all three:

• Code-based checks. Deterministic rules: valid JSON, under the word limit, no banned phrase. Cheap, fast, exact. Use these wherever the answer is mechanical.
• Human review. A person grades against your rubric. Slow and expensive, but the gold standard for judgment calls like tone. Use it on a sample, not everything.
• LLM-as-judge. A second model grades the first against your rubric. Scales like code, handles nuance like a human. I cover how to do this well in eval-driven development for AI products.

Start with code-based checks for anything mechanical, then layer human or LLM grading on the judgment calls.

Step 4: Run evals on every change

A one-time eval is a snapshot. The value comes from running it on every change: new prompt, new model version, new retrieval source. AI systems break sideways, and a swap that improves one thing often quietly degrades another. Only a standing eval suite catches the regression.

Wire your evals into the same place you'd put tests, so a drop in score blocks a release the way a failing test does.

Common mistakes

The patterns I see most: grading on vibes with no rubric, so scores mean whatever the grader felt that day; synthetic-only datasets that never appear in real traffic; collapsing accuracy, tone, and safety into one number that hides the trade-off you need to see; and evals built once for a launch, then abandoned, so the next regression ships unnoticed.

Where to start

You don't need a platform to begin. Start with five real inputs, a one-paragraph rubric, and a spreadsheet. Score this week's version, change one thing, score again. That loop is already an eval.

When you want structure, my free AI Eval Builder walks you through defining the task, criteria, and test cases. If the concept is new, start with what is an AI eval. And if the harder problem is getting your whole team to adopt this, that's AI adoption consulting.