AI evals resources for product teams.

A six-step methodology for AI evaluation where teams earn each step before automating, ending with deterministic checks and a surgical, validated LLM-as-judge built on documented failure patterns.

An AI feature is one capability inside a product that already does a job. An AI product is the whole thing built around the model. The distinction decides what a mediocre model output actually costs — being ignored, or eroding trust in the surrounding product.

Productivity use is you using an LLM to move faster in your own work, where a human reads every output. AI at scale is the same model running inside your product thousands of times a day with nobody checking. The techniques that work for one break silently in the other.

The pattern of deploying AI features based on a handful of happy-path demos, without systematic validation, and hoping production matches the demo. The default mode for most teams shipping their first AI feature.