Everyone is racing to ship AI features. Almost nobody is measuring whether they actually work.

That needs to change.

When I talk to engineering teams building with LLMs, the conversation usually follows a pattern: they demoed something impressive, shipped it fast, and are now quietly nervous about what it’s doing in production. They have vibes. They have screenshots. They don’t have numbers.

That’s what evals are for.

An eval is just a systematic way to answer the question: is this AI doing what I think it’s doing? It can be as simple as a spreadsheet of test cases and expected outputs, or as sophisticated as an automated pipeline that runs thousands of checks on every model update. The form doesn’t matter much. The discipline does.

Why teams skip it

It’s not laziness. It’s that evals feel like overhead when you’re moving fast, and the payoff isn’t obvious until you’ve been burned.

The burn usually looks like one of these:

• You change a prompt to fix one behavior and silently break three others
• You upgrade to a new model version and don’t notice a regression until a user reports it
• You ship a feature that works great on your test data and fails consistently on real data

All of these are eval problems. All of them are preventable.

Where to start

If you’re not doing evals at all, the bar is low: just write down what “good” looks like for your use case. Literally. Before you change anything, capture 20 real examples of inputs and what the correct output should be. Run your system against them. Save the results.

That’s it. You now have a baseline. Everything you do from here can be measured against it.

More on this soon — including what I’ve found actually works in practice, what doesn’t, and the tools worth paying attention to.