validate-evaluator

To calibrate an LLM judge against human labels, run the evaluator calibration process which computes agreement metrics like Cohen's kappa, precision, recall, and F1 on a labeled dataset to quantify judging reliability.

LLM evaluator reliability is measured using TPR, TNR, precision, recall, F1, and Cohen's kappa. These metrics quantify how accurately an AI judge scores data compared to a ground truth human-labeled dataset.

To detect systematic bias in an LLM evaluation pipeline, apply bias-detection algorithms during evaluator calibration to identify scoring anomalies and generate targeted prompt corrections for the AI judge.

Yes, you can get confidence intervals for AI judge calibration scores. The calibration process generates a comprehensive report that includes confidence intervals and actionable deployment guidance for real-world judging tasks.

The best way to improve LLM judge accuracy on varying quality datasets is to apply evaluator calibration, which detects systematic biases and produces recommended prompt corrections to align the LLM judge with human labels.

Yes, you need a human-labeled dataset to measure LLM judge agreement. The calibration process requires ground truth labels to compute metrics like Cohen's kappa and identify biases in the LLM evaluator's scoring behavior.