Evals are the product
Teams keep building AI demos that work in the notebook and fail in production. The difference is not the model. It's whether the team wrote the evals first.
A lot of AI products that stall have the same shape: the demo worked, the production version hallucinates, costs more than expected, and the operator team has no way to intervene.
The root cause is almost always the same. The team built the product first and reached for evals when things started breaking. By then the evals are too late — they measure what the product happens to do today, not what the product is supposed to do.
Write the eval first
Before the first prompt is written, the team should be able to answer: what does correct look like, on what data, to what tolerance?
An eval is not a test suite. It’s a structured contract between the model and the operator: here are the inputs we accept, here is the behavior we expect, here is the metric we track.
When the eval comes first, two things get much easier:
1. Model swaps are cheap. A new Anthropic model ships; you rerun the eval and know in ten minutes whether to switch. Without evals, this takes a sprint.
2. Operators can intervene. If the model crosses a threshold, route to a human. That routing surface is only possible if you have a number to threshold on.
What we ship
Every AI product engagement ships four things: the prompt and tool-use architecture, the retrieval pipeline, the eval harness, and the operator surface for routing or override. None of them is optional. An AI product without evals is a demo.