Validate Evaluator

Calibrate an LLM judge against human judgment.

Overview

1. Split human-labeled data into train (10-20%), dev (40-45%), test (40-45%)
2. Run judge on dev set and measure TPR/TNR
3. Iterate on the judge until TPR and TNR > 90% on dev set
4. Run once on held-out test set for final TPR/TNR
5. Apply bias correction formula to production data

Prerequisites

• A built LLM judge prompt (from write-judge-prompt)
• Human-labeled data: ~100 traces with binary Pass/Fail labels per failure mode
  • Aim for ~50 Pass and ~50 Fail (balanced, even if real distribution is skewed)
  • Labels must come from a domain expert, not outsourced annotators
• Candidate few-shot examples from your labeled data