Evals are the new tests

In traditional software, a test suite gives you a binary signal: the code does what it is specified to do, or it does not. The discipline of test-driven development built an entire culture around this signal — you write the test before the code, you keep the suite green, you treat a failing test as a blocker. That culture exists because the signal is reliable enough to be worth trusting.

AI features break this model. The output of an LLM is not binary; it exists on a spectrum of quality, and "correct" is often a matter of degree. A unit test cannot tell you whether your customer support agent is being appropriately empathetic, or whether your summarization pipeline is losing the most important information, or whether your code assistant is subtly introducing security vulnerabilities. You need a different kind of signal — and that signal is an eval.

An eval is a structured benchmark: a set of inputs with expected outputs or quality criteria, run against your system on a schedule, with results tracked over time. Good evals share a lot of DNA with good tests — they are specific, repeatable, and owned by someone — but they require additional ingredients: a rubric for what "good" means, a representative sample of real-world inputs, and often a judge (human or model-based) to score outputs that cannot be evaluated with string matching.

The practical implication is that shipping an AI feature without evals is the equivalent of shipping software without tests — except the consequences are often harder to detect and slower to surface. Start with a small eval set on day one, even if it is just twenty examples scored manually. The investment pays off the first time a model update silently regresses your quality metrics, and you catch it before your users do.