Does it support statistical significance testing?

Yes, it includes a built-in A/B testing framework that calculates p-values and Cohen's d effect sizes to ensure your improvements are statistically valid.

What metrics are supported for RAG applications?

The skill includes RAG-specific metrics like Mean Reciprocal Rank (MRR), NDCG, and Precision@K, along with custom checks for groundedness and factuality.

How does it detect performance regressions?

It features a dedicated Regression Detector class that compares current test results against a baseline and flags any metrics that drop below a configurable threshold.

Can I use this for model-to-model comparisons?

Yes, it provides pairwise comparison patterns using an LLM-as-judge approach to determine which model or prompt variation produces better results.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Implements systematic evaluation strategies for AI applications using automated metrics, human feedback loops, and LLM-as-judge patterns.

About

This skill provides a comprehensive toolkit for measuring and improving the performance of Large Language Model applications. It guides developers through the implementation of standard NLP metrics like BLEU and ROUGE, sophisticated embedding-based scores like BERTScore, and modern LLM-as-judge evaluation patterns. Whether you are building RAG pipelines, chatbots, or classification agents, this skill helps establish rigorous baselines, perform A/B testing with statistical significance, and detect performance regressions before they reach production. It bridges the gap between raw model output and production-grade reliability by quantifying quality across dimensions like accuracy, groundedness, and coherence.

Key Features

RAG-specific evaluation for retrieval quality and groundedness
1 GitHub stars
Automated metrics implementation including BLEU, ROUGE, and BERTScore
Automated regression detection to prevent performance degradation
LLM-as-judge patterns for pointwise and pairwise model comparisons
Statistical A/B testing framework with p-value and Cohen's d analysis

Use Cases

Validating RAG system accuracy and retrieval relevance
Establishing quality benchmarks for production AI agents
Comparing performance between different model versions or prompt strategies

About

Key Features

RAG-specific evaluation for retrieval quality and groundedness
1 GitHub stars
Automated metrics implementation including BLEU, ROUGE, and BERTScore
Automated regression detection to prevent performance degradation
LLM-as-judge patterns for pointwise and pairwise model comparisons
Statistical A/B testing framework with p-value and Cohen's d analysis

Use Cases

Validating RAG system accuracy and retrieval relevance
Establishing quality benchmarks for production AI agents
Comparing performance between different model versions or prompt strategies