Can I use this for production monitoring?

Yes, it includes patterns for online evaluation, A/B testing variants, and tracking business metrics like user satisfaction and cost per interaction.

How does this skill help with RAG systems?

It provides specific metrics like faithfulness, answer relevance, and context precision to ensure your RAG pipeline provides accurate, grounded answers based on retrieved data.

Does it support safety and bias testing?

Yes, it includes methodologies for detecting toxic content, measuring demographic bias, and verifying factual accuracy through consistency checks.

What is the LLM-as-judge pattern?

It uses a powerful model, such as Claude 3.5 Sonnet or Opus, to grade the outputs of other models or prompts based on specific rubrics, providing a scalable alternative to human review.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: ancoleman

byancoleman

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158

Seguridad y Pruebas

Evaluates Large Language Model performance using automated metrics, LLM-as-judge patterns, and RAG validation frameworks.

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This skill provides a comprehensive methodology for assessing the quality, safety, and reliability of LLM systems. It enables developers to implement structured testing strategies ranging from simple unit evaluations and classification metrics to sophisticated RAGAS scoring for retrieval-augmented pipelines. By leveraging layered evaluation patterns—including automated checks, LLM-as-judge scoring, and safety audits for hallucinations or bias—it ensures that AI applications meet production-grade standards and performance benchmarks.

Características Principales

LLM-as-judge scoring patterns with customizable quality rubrics
158 GitHub stars
RAG pipeline validation using RAGAS (Faithfulness, Relevance, Precision)
Production monitoring strategies for A/B testing and user feedback
Standardized benchmark integration for MMLU, HumanEval, and GPQA
Safety and alignment testing for hallucination and bias detection

Casos de Uso

Comparing model performance and cost-efficiency during A/B testing
Validating RAG system accuracy and grounding to prevent hallucinations
Integrating automated quality gates into CI/CD pipelines for AI applications

Acerca de

Características Principales

LLM-as-judge scoring patterns with customizable quality rubrics
158 GitHub stars
RAG pipeline validation using RAGAS (Faithfulness, Relevance, Precision)
Production monitoring strategies for A/B testing and user feedback
Standardized benchmark integration for MMLU, HumanEval, and GPQA
Safety and alignment testing for hallucination and bias detection

Casos de Uso

Comparing model performance and cost-efficiency during A/B testing
Validating RAG system accuracy and grounding to prevent hallucinations
Integrating automated quality gates into CI/CD pipelines for AI applications