The llm-evaluation skill provides a systematic approach to measuring and improving the quality of AI-driven applications. It enables developers to implement a multi-layered evaluation strategy encompassing automated text metrics (BLEU, ROUGE, BERTScore), LLM-as-Judge patterns for semantic assessment, and structured human evaluation frameworks. By integrating statistical A/B testing and regression detection, this skill helps teams confidently validate prompt changes, compare model performance, and ensure production-grade reliability across text generation, classification, and RAG tasks.
Key Features
01LLM-as-Judge patterns for automated pointwise and pairwise evaluation
02Statistical A/B testing framework with Cohen's d effect size calculation
03Automated metrics for text generation and retrieval (RAG) performance
04Regression detection to prevent performance drops during deployment
05Human annotation structures with inter-rater agreement (Cohen's Kappa) analysis
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Use Cases
01Systematically comparing different foundation models or prompt iterations
02Validating RAG system accuracy, groundedness, and retrieval relevance
03Building automated CI/CD pipelines to catch LLM performance regressions
