Human Evaluation for AI

Human evaluation for AI encompasses structured methodologies for trained human raters to assess model output quality against defined rubrics.

It includes rubric design, annotator calibration, and inter-annotator agreement measurement. Human evaluation remains irreplaceable by automated metrics in tasks where quality is subjective, contextually nuanced, or safety-critical. Also known as: Human Eval, Human-in-the-Loop Evaluation

What this topic covers

Foundations — Human evaluation for AI is more complex than it appears: scoring model outputs reliably requires carefully designed rubrics, annotator calibration, and statistical measures of agreement that expose how much human judgment actually varies.
Implementation — These guides walk through building annotation pipelines from scratch, covering tool selection, rubric specification, quality control, and the trade-offs between rater cost and annotation consistency.
What's changing — LLM judges are displacing human raters at scale, but not everywhere.
Risks & limits — Human evaluation introduces its own risks: annotator bias, unclear rubrics, and the welfare of annotators themselves.

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