LLM Application Evaluation

关于

This skill provides a structured methodology for measuring the performance, quality, and reliability of Large Language Model applications. It offers implementation patterns for traditional NLP metrics like BLEU and ROUGE, modern embedding-based assessments like BERTScore, and advanced 'LLM-as-Judge' techniques. Whether you are validating RAG pipelines, comparing model versions, or establishing regression testing in a CI/CD environment, this skill equips you with the statistical tools and code patterns needed to build production-grade AI systems with confidence.

主要功能

• Statistical A/B testing framework with Cohen’s d and p-value analysis
• RAG-specific evaluation patterns for retrieval and groundedness
• LLM-as-Judge scoring for automated qualitative assessment
• Regression detection to identify performance drops between model versions
• 0 GitHub stars
• Implementation of automated metrics including BLEU, ROUGE, and BERTScore

使用场景

• Validating the accuracy and factuality of RAG-based search systems
• Benchmarking a new prompt or model version against a production baseline
• Establishing automated quality gates for AI features in deployment pipelines