Every LLM feature that fails in production fails the same way: it shipped without a measurable definition of 'good.' Demo-quality output convinces the room. Then the long tail of real prompts arrives, the team has no harness to measure regression, and the feature quietly degrades until someone calls it broken.
The fix is unsexy and structural. Before the first prompt is written, you write the eval. A small, curated set — 30 to 240 cases — that pins down the contract: what the model should do, what it must not do, and what the acceptable boundary looks like.
On a recent Northstar Labs engagement we ran the eval suite for two weeks before the model team wrote a single production prompt. By the time prompts existed, every change was scored. Every regression was caught before deploy. The team shipped with 78% weekly active adoption in eight weeks — and zero uncited claims in production.
The discipline matters more than the tooling. Treat eval cases as code. Version them. Run them in CI. Score them on every PR. When the score regresses, the PR doesn't merge.
Once that loop exists, the rest of the system gets easier. You can A/B prompts, swap models, change retrieval, refactor chains — and you'll know within minutes whether quality moved. That's the difference between an AI feature that ages well and one that decays the moment a model version rolls.
