How does the 'LLM-as-Judge' functionality work?

It utilizes stronger models to evaluate weaker model outputs through structured prompts, allowing for automated qualitative scoring of accuracy, helpfulness, and clarity.

Which automated metrics are supported by this skill?

The skill supports a wide range of metrics including BLEU, ROUGE, METEOR, BERTScore for text; Accuracy, F1, and AUC-ROC for classification; and MRR, NDCG, and Precision@K for RAG systems.

How does it handle regression testing?

It provides a RegressionDetector class that compares new evaluation results against a stored baseline, flagging any metric that drops beyond a configurable threshold.

Can this skill help with RAG (Retrieval-Augmented Generation) testing?

Yes, it includes specific patterns for measuring retrieval quality and 'groundedness'—ensuring that the model's response is actually supported by the provided context.

Is there support for statistical significance in model comparisons?

Yes, the skill includes an A/B testing framework that calculates p-values and Cohen's d effect sizes to determine if improvements are statistically significant.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: Microck

byMicrock

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데이터 과학 및 ML

Implements robust evaluation strategies for large language model applications using automated metrics, human feedback, and LLM-as-judge patterns.

소개

The llm-evaluation skill provides a comprehensive toolkit for measuring and improving the quality of AI applications by moving beyond anecdotal testing to systematic measurement. It enables developers to implement standard NLP metrics like BLEU and ROUGE, leverage advanced embedding-based scores like BERTScore, and utilize 'LLM-as-Judge' patterns for automated qualitative assessment. By integrating regression detection, statistical A/B testing frameworks, and benchmarking suites, this skill ensures that model updates, prompt changes, and RAG pipeline adjustments result in measurable improvements rather than silent performance regressions.

주요 기능

81 GitHub stars
Regression detection system to identify and prevent performance drops before deployment.
Comprehensive automated metrics for text generation, classification, and RAG retrieval.
Advanced LLM-as-Judge patterns for both pointwise scoring and pairwise comparisons.
Statistical A/B testing framework using Cohen's d and t-tests to validate model improvements.
Structured human evaluation frameworks with inter-rater agreement (Cohen's Kappa) calculations.

사용 사례

Establishing automated CI/CD evaluation pipelines to catch regressions in AI behavior.
Systematically comparing output quality across different foundation models or prompt versions.
Validating RAG system performance by measuring groundedness, relevance, and retrieval accuracy.

소개

주요 기능

81 GitHub stars
Regression detection system to identify and prevent performance drops before deployment.
Comprehensive automated metrics for text generation, classification, and RAG retrieval.
Advanced LLM-as-Judge patterns for both pointwise scoring and pairwise comparisons.
Statistical A/B testing framework using Cohen's d and t-tests to validate model improvements.
Structured human evaluation frameworks with inter-rater agreement (Cohen's Kappa) calculations.

사용 사례

Establishing automated CI/CD evaluation pipelines to catch regressions in AI behavior.
Systematically comparing output quality across different foundation models or prompt versions.
Validating RAG system performance by measuring groundedness, relevance, and retrieval accuracy.