AI evaluation

AI evaluation is the systematic measurement of LLM output quality across criteria such as voice match, factual accuracy, format compliance, brand-fit, and constraint adherence.

Evals run on a sample of outputs against a fixed set of criteria, usually with a mix of automated scoring (deterministic checks, judge-model scoring) and human review for borderline cases. Common eval dimensions in marketing tools include voice similarity, banned-cliche presence, format compliance (does the carousel have a cover slide?), and length appropriateness for the channel.

A tool that runs evals continuously can detect regressions when prompts change, when the underlying model updates, or when brand inputs drift. Tools that don’t are essentially shipping changes blind and finding out about regressions when the user complains.

AI quality drifts silently as models change. Evaluation is the only way to know whether your AI marketing tool got better, worse, or stayed the same after the latest update.