Human-in-the-Loop Nugget Annotation for Accountable LLM-as-a-Judge Evaluations

Evaluating AI/Agentic system outputs reliably requires human judgment, but how one incorporates the human determines whether one gets a real quality signal or expensive theater. The common approaches either accidentally anchor human experts (leading to rubber-stamping) or leave them unsupported in cognitively demanding labeling tasks. We present a prototype of an annotation tool that implements a different division of labor: humans identify what information matters (nuggets), while LLMs handle high-volume matching of nuggets to system outputs. This plays to each party's strengths while maintaining genuine human oversight. We describe the Human-AI workflow, key design decisions, and how resulting nugget banks are used with automated judges.