LLM Evaluation & Benchmarking

소개

This skill empowers developers to systematically measure and improve LLM application quality by providing a robust toolkit for performance assessment. It bridges the gap between raw model outputs and production-ready reliability through the implementation of standard metrics like BERTScore, ROUGE, and BLEU, alongside advanced 'LLM-as-Judge' patterns and statistical testing. Whether you are detecting performance regressions, validating prompt improvements, or benchmarking different models, this skill provides the necessary patterns to establish baselines and maintain high standards for AI-driven features.

주요 기능

• Statistical A/B testing frameworks featuring T-tests and Cohen's d analysis
• Regression detection to identify performance drops before deployment
• 0 GitHub stars
• Automated NLP metrics including BLEU, ROUGE, METEOR, and BERTScore
• Retrieval-specific metrics for RAG systems like MRR, NDCG, and Precision@K
• LLM-as-Judge implementation for automated qualitative and pairwise assessments

사용 사례

• Comparing the performance and accuracy of different model providers (e.g., OpenAI vs Anthropic)
• Measuring the groundedness and factuality of RAG-based search results
• Validating prompt engineering changes to ensure no loss in quality