Shipping AI that works: evals before features
Most AI projects stall in the gap between an impressive demo and a system people can rely on. The demo works because someone drove it carefully. Production fails because real users do not.
Evals are the contract
Before we build a feature, we build the way to measure it. An evaluation harness turns "it seems good" into a number you can defend and improve. It is the difference between shipping on vibes and shipping on evidence.
Three rules we follow
- Measure first. Define the eval set before writing the feature.
- Gate releases. No deploy that regresses the eval score.
- Keep humans in the loop where the cost of being wrong is high.
Done well, this is not bureaucracy — it is the fastest path to an AI system you can trust.