Human Anchors for Judge Scores
Use human-labeled anchor sets to calibrate LLM-as-judge RAG evaluation.
A judge score without human anchors is only a guess at your standard. Calibrate before you automate LLM judges can grade RAG dimensions such as relevance, groundedness, and correctness at useful speed. But they inherit ambiguity from the rubric and bias from the judge model. ARES shows a practical pattern: combine automated evaluation with a smaller human-annotated set so the automated scorer is checked against expert labels. The anchor set is the stabilizer. It contains examples your team agrees are pass, fail, and borderline. You run the judge on those examples and inspect where it disagrees. Agreement metrics help summarize…
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