LLM-as-Judge Evaluator
Evaluates LLM-generated text, code, and answers across multi-dimensional criteria including accuracy, relevance, completeness, and tone. Also useful for comparing outputs across models.
/judgeAI engineer? Run /judge to quantitatively compare output quality before and after prompt changes
PM managing AI feature quality? Systematically validate AI response quality before launch
How It Works
Skill Code
Copy and paste into your CLAUDE.md to start using immediately.
How LLM-as-Judge Evaluator Works
LLM-as-Judge evaluates AI-generated outputs against structured rubrics — factual accuracy, relevance, coherence, safety — scores each dimension independently, and provides comparative rankings when evaluating multiple model outputs.
When to Use LLM-as-Judge Evaluator
Critical for AI product teams that need to evaluate prompt changes, model upgrades, or fine-tuning results systematically — replaces subjective 'vibes-based' evaluation with reproducible, criteria-based scoring.
Key Strengths
- Evaluates across multiple quality dimensions independently
- Provides reproducible scoring with structured rubrics
- Enables comparative ranking of multiple model outputs
- Replaces subjective evaluation with systematic methodology