Which automated metrics does this skill support?

It supports a wide range of metrics including BLEU, ROUGE, and METEOR for text generation, BERTScore for semantic similarity, and RAG-specific metrics like MRR and NDCG.

Does it support statistical analysis for model comparisons?

Yes, it includes an A/B testing framework that calculates T-tests, p-values, and Cohen's d to determine if improvements are statistically significant.

How does this skill help with RAG evaluation?

It provides specific metrics for measuring retrieval accuracy, groundedness, and context relevance, ensuring that RAG systems are providing factually supported answers.

Can I use one LLM to evaluate another?

Yes, the skill includes 'LLM-as-judge' patterns for both single-output scoring (pointwise) and pairwise comparisons to determine which model response is superior.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: Berkay2002

byBerkay2002

数据科学与机器学习

Implements comprehensive evaluation strategies for Large Language Models using automated metrics, human feedback, and comparative benchmarking.

关于

The llm-evaluation skill provides a robust toolkit for systematically assessing the performance, quality, and reliability of AI applications. It enables developers to implement standard automated metrics like BLEU and BERTScore, establish sophisticated LLM-as-judge patterns for semantic quality, and perform rigorous A/B testing with statistical significance. By bridging the gap between raw model output and production-grade reliability, this skill helps teams validate prompt engineering changes, detect regressions early in the development lifecycle, and build verifiable confidence in complex agentic or RAG-based systems.

主要功能

Statistical A/B testing framework with Cohen’s d effect size analysis
0 GitHub stars
Automated regression detection to identify performance drops before deployment
Implementation of automated metrics including BLEU, ROUGE, and BERTScore
LLM-as-judge patterns for pointwise and pairwise model comparison
Retrieval-specific metrics for RAG systems like MRR, NDCG, and groundedness

使用场景

Detecting performance regressions in production AI pipelines
Measuring and validating RAG system performance for specialized domain knowledge
Comparing model responses across different prompts or fine-tuning iterations

关于

主要功能

Statistical A/B testing framework with Cohen’s d effect size analysis
0 GitHub stars
Automated regression detection to identify performance drops before deployment
Implementation of automated metrics including BLEU, ROUGE, and BERTScore
LLM-as-judge patterns for pointwise and pairwise model comparison
Retrieval-specific metrics for RAG systems like MRR, NDCG, and groundedness

使用场景

Detecting performance regressions in production AI pipelines
Measuring and validating RAG system performance for specialized domain knowledge
Comparing model responses across different prompts or fine-tuning iterations