Ship the eval before the feature

Every stalled enterprise AI project we've been asked to rescue shares a trait: the team can't say, with numbers, whether yesterday's change made the system better or worse. Demos convinced the stakeholders; nothing convinces the engineers.

So the first artifact we build is never the feature. It's the eval, a suite of real inputs with known-good outputs, run on every change, gating every release. It feels slower for about a week. Then it's the fastest thing about the project.

What an eval buys you

Three things, in order of when you notice them. First, honest iteration: prompt and model changes become measurable diffs instead of vibes. Second, regression safety: the model upgrade that silently breaks your edge cases gets caught in CI, not in a customer email. Third, the one executives care about, a defensible claim: when someone asks “how do you know it works?”, you have an answer that survives a follow-up question.

What it looks like in practice

For commerce intelligence, the eval suite scores bundle-copy generation against merchandising rules and tone constraints, and mining output against held-back order data. For compliance work, it's regulatory questions with citations checked against known-correct answers. The harness is rarely more than a few hundred cases, coverage of the failure modes that matter beats volume.

The habit generalizes: if a system's behavior matters enough to ship, it matters enough to measure. That's why 100% of systems we build are eval-driven. No exceptions, including production AI systems, where nobody would notice but us.