LLM Evaluation Framework

소개

This skill empowers developers to systematically measure and improve LLM performance through a comprehensive multi-layered approach. It provides standardized implementations for automated metrics like BLEU and BERTScore, sophisticated LLM-as-judge patterns for semantic quality, and statistical frameworks for A/B testing and regression detection. By integrating these strategies, teams can move beyond anecdotal testing to data-driven model selection, prompt engineering, and production monitoring, ensuring reliable AI outputs and preventing performance regressions over time.

주요 기능

• Statistical A/B testing framework with Cohen's d effect size calculations
• Human evaluation frameworks with inter-rater agreement (Cohen's Kappa) tools
• 2 GitHub stars
• Comprehensive automated metrics suite including BLEU, ROUGE, and BERTScore
• LLM-as-Judge patterns for automated semantic scoring and pairwise comparisons
• Automated regression detection to prevent quality drops during model updates

사용 사례

• Automating quality assurance and groundedness checks for RAG systems
• Establishing performance baselines to detect subtle regressions in production AI
• Benchmarking different LLM models or prompt iterations before deployment