What AI evaluation means
AI evaluation is how you decide whether an AI output is good. It turns a subjective sense of quality into a measurable verdict: pass or fail, a score, or a category, judged against criteria you set in advance.
Evaluation can happen offline (testing a prompt or model against a fixed dataset before shipping) or online (checking live outputs in production as they are generated). Monitoring automations is the online case.
How outputs get evaluated
Evaluations use a mix of methods: deterministic checks for literal requirements, model-based scoring (an LLM as a judge) for semantic ones, and human review for the cases that need a person. Each output ends up with a verdict and ideally an explanation of why it passed or failed.
Aggregated over time, evaluations become metrics: pass rate per workflow, failure types, and trends that reveal when a prompt or model has drifted.
Put AI evaluation into practice with Tracira
Tracira adds output monitoring, plain-English guardrails, and human approval to your Make and n8n automations. One webhook, no code, free to start.
