Individual skill
llm-as-judge-evaluation
Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.
backend/cli/skills/llm-tools/llm-as-judge-evaluation/SKILL.md
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- Nov 6, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- evaluation Declared skill tag
- llm-as-judge Declared skill tag
- pairwise-comparison Declared skill tag
- quality-assessment Declared skill tag
- rubric-design Declared skill tag
- model-comparison Declared skill tag
- automated-evaluation Declared skill tag
- design Derived category
- data Derived category
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# LLM-as-Judge Evaluation
## When to Use This Skill
Use LLM-as-Judge evaluation when you need to:
- **Compare a fine-tuned model vs frontier** — Does the student beat the teacher on your task?
- **Quality gates before deployment** — Automated go/no-go on model releases
- **Continuous evaluation** — Monitor production model quality over time
- **Generate preference data** — Create (chosen, rejected) pairs for DPO/RLHF training
- **Evaluate without ground truth** — When exact answers don't exist (creative, open-ended tasks)
### When NOT to Use
- Tasks with verifiable answers (math, code execution) — use exact match or unit tests
- Extremely simple classification — use accuracy/F1 directly