LLM Evaluation Framework

소개

This skill provides a robust framework for measuring and improving the quality of Large Language Model applications through systematic testing and validation. It equips developers with the tools to implement automated metrics like BLEU, ROUGE, and BERTScore, establish human-in-the-loop evaluation pipelines, and utilize sophisticated 'LLM-as-Judge' patterns for nuanced qualitative assessment. By integrating regression testing and A/B testing methodologies, it ensures that model updates, prompt changes, or architectural shifts consistently improve performance while preventing quality regressions in production environments.

주요 기능

• RAG-specific metrics like MRR, NDCG, and Groundedness checks
• 0 GitHub stars
• Regression detection to prevent performance drops during updates
• Automated metrics including BLEU, ROUGE, BERTScore, and Perplexity
• LLM-as-Judge patterns for single output and pairwise comparison
• Statistical A/B testing framework with Cohen's d effect size calculation

사용 사례

• Measuring retrieval quality and factual accuracy in RAG-based systems
• Validating prompt engineering improvements and monitoring for hallucinations
• Comparing performance between different foundation models or fine-tuned versions