The Lovelaice glossary

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.

AI quality issues that produce confident-looking but useless or wrong output. They don't trigger errors or user reports — users just lose trust and quietly stop using the feature, so the failure never appears in your metrics.

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.

Eyeballing a handful of AI outputs and concluding the model 'seems fine.' The dominant industry practice for AI quality assessment — and the practice that systematic evaluation replaces.