---
title: "Use LLM-as-a-Judge Carefully"
description: "Design rubrics, randomize ordering, calibrate against humans, and keep deterministic checks first."
language: en
canonical: https://navinresearch.com/blog/safety-evaluation/llm-as-judge.md
topics: ["LLM as a judge", "model-based evaluation", "AI evaluation rubric"]
---

# Use LLM-as-a-Judge Carefully

Design rubrics, randomize ordering, calibrate against humans, and keep deterministic checks first.

**Short answer:** Model judges are useful for nuanced qualities but can be biased by style, length, position, or self-preference. Give a concrete rubric and evidence, hide irrelevant identity, randomize pair order, and validate judge agreement against expert labels.

## Implementation steps

1. Define dimensions and anchored score examples.
2. Use deterministic graders before subjective judging.
3. Run pairwise order swaps and repeated judgments.
4. Escalate low-agreement or high-risk cases to humans.

## Validation checklist

- Measure agreement and bias by answer length and model source.
- Keep a private calibration set.
- Version judge model, prompt, and rubric.

## Common mistakes

- One vague 'which is better?' prompt.
- Using the candidate model as its only judge.
- Reporting judge scores as objective truth.

## Practical decision

Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.

## Official sources

- [OpenAI, Evaluations guide](https://platform.openai.com/docs/guides/evals)
- [Anthropic, Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)

---

Reviewed: 2026-08-24. This concise engineering note is educational, not a claim that Navin Research created or independently verified the cited provider technology.