Advanced LLM Evaluation

About

This skill provides a comprehensive framework for building reliable LLM evaluation systems using automated judge models. It bridges the gap between raw model outputs and production-ready quality by implementing research-backed techniques like pairwise comparison, direct scoring rubrics, and systematic bias mitigation. Whether you are debugging inconsistent results or setting up A/B testing pipelines, this skill equips you with the metrics, prompt structures, and decision frameworks necessary to achieve high-fidelity automated assessments that correlate closely with human judgment.

Key Features

• Implementation of Direct Scoring and Pairwise Comparison patterns
• Automated rubric generation with domain-specific calibration
• Mitigation strategies for Position, Length, and Authority biases
• 7,140 GitHub stars
• Standardized metrics selection including F1, Cohen's κ, and Spearman's ρ
• Structured evaluation pipeline design for production environments

Use Cases

• Building automated CI/CD pipelines for LLM prompt engineering and regression testing
• Conducting side-by-side A/B model comparisons for product performance validation
• Creating standardized quality rubrics for specialized domains like legal, medical, or technical content