North Star for product
Define what good means, own the scorers, and see the metrics. Without writing code or waiting on engineering.
Define what good means, own the scorers, and see the metrics. Without writing code or waiting on engineering.
You updated a prompt.
A case that used to work now fails.
You shipped a release.
A user caught your AI making up a fact.
You added a skill.
Your model calls it sometimes, ignores it others.
You switched the model.
Costs doubled but you can't tell if quality increased.
The same document that defines the feature also measures it.
Write the seed once. North Star synthesizes the dataset and derives the scorers from it, so what you asked for is exactly what gets measured.
The conditions you'd check in review become graders that run on every change. Your definition of done, automated.
Three moves, on repeat.
Write the seed. The dataset and scorers derive from it.
Every change is graded against your bar, automatically.
Production data comes back labeled. Reprioritize by impact.
Own the parts that define quality.
Define good in plain language. No Python, no judge harness.
Versioned and yours. Experiment and roll back without touching code.
Try a model, run the evaluations, see the score, ship.
Tie quality to the metric that matters.
Read quality scores against the business metric.
Spend effort where it moves the number.