Ship the Eval Before the Feature

Every AI feature ships twice. Once in the demo, where it works. And once in production, where you find out whether it actually does.

The teams that survive that second shipping have one habit in common: they built the measurement before they built the feature. Not after the first incident. Not "next sprint." Before.

The demo is not evidence

A demo proves that a good outcome is possible. Production asks a different question: how often is the outcome good, on your real inputs, at your real volume, after the model provider silently updates something on a Tuesday?

If you can't answer that with a number, you don't have an AI feature. You have an anecdote with a UI.

What "eval first" actually means

Before the feature ships, three things exist:

1. A dataset that looks like your traffic. Not a benchmark from a paper — a few hundred real (or realistic) inputs, including the ugly ones, with agreed-on definitions of what a good output looks like.
2. A harness that runs without a human. Every prompt change, model swap, or dependency bump runs the dataset and produces a score. If the score is manual, it will stop happening the first busy week.
3. A gate. A threshold below which the change doesn't ship. This is the part most teams skip, and it's the part that makes the other two matter.

The compounding return

The eval harness feels like overhead exactly once. From then on it's leverage: you can swap models when a cheaper one appears, tighten prompts without a prayer circle, and answer "did it get worse?" in minutes instead of support tickets.

Quality drift becomes a failed build, not a customer complaint. That's the whole trade, and it's available to any team willing to do the work in the right order.

Ship the eval. Then ship the feature.