LLM Evaluation & Testing

Acerca de

This skill provides a comprehensive framework for validating LLM application outputs using a multi-layered evaluation approach. It enables developers to implement traditional metrics like ROUGE and BERTScore alongside advanced LLM-as-Judge patterns for nuanced, rubric-based scoring. Designed for production-grade AI development, it includes specialized modules for hallucination detection and RAG grounding checks, making it essential for building reliable benchmarks and establishing continuous evaluation pipelines within CI/CD workflows.

Características Principales

• LLM-as-Judge patterns for rubric-based scoring and pairwise comparisons
• Standardized evaluation dataset structures for systematic benchmarking
• 18 GitHub stars
• Implementation of automated metrics including ROUGE, BERTScore, and Perplexity
• Hallucination detection and RAG grounding verification methods
• Binary pass/fail logic for automated regression testing

Casos de Uso

• Integrating automated AI quality gates into CI/CD deployment pipelines
• A/B testing prompts to determine which version yields higher quality outputs
• Measuring the factual accuracy and retrieval quality of RAG-based systems