Enterprise LLM Evals · Lesson 11
Human Feedback & Judge Calibration
How to make automated evals trustworthy enough to use
Human Feedback & Judge Calibration
Lesson 11 — how to make automated evals trustworthy enough to use
- Why human review is not replaced by LLM judges — it trains and audits them
- The calibration loop: sample, label, judge, compare, improve
- Review modes: single scoring, pairwise, annotation queues, user feedback
- Why overall agreement percentages can hide dangerous failures
Core idea
Human review is not replaced by LLM judges; it trains and audits them.
1. Why Human Calibration Matters
LLM judges are scalable, but they can be biased, inconsistent, overly lenient, or reward verbosity. Human labels create the anchor that tells you whether your automated judge is trustworthy.
2. The Calibration Loop
This is a continuous process, not a one-time setup. As the application evolves and new failure modes appear, the judge needs re-calibration.
3. Review Modes
| Mode | Best for |
|---|---|
| Single-output scoring | Policy compliance, rubric labels, pass/fail |
| Pairwise preference | Choosing better prompt/model versions |
| Expert annotation queue | Domain correctness — legal, medical, financial, HR |
| User feedback | Production satisfaction signal — not ground truth by itself |
4. The Dangerous Metric
A judge with 90% agreement with humans overall may still fail badly on the highest-risk 10%. Always segment agreement by category, risk level, and failure type. "Overall agreement" can mask dangerous blind spots.
Sources: Langfuse: Scores Overview, Phoenix: LLM Evals
Q1: Your LLM judge has 92% agreement with human reviewers overall. Is it safe to automate?
Show answer
Not necessarily. Check agreement by category. If the 8% disagreement is concentrated on high-risk cases (safety, compliance, PII), the judge is not safe to automate for those categories. Segment before you trust.
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