What automated metrics are supported for text generation?

The skill supports a wide range of metrics including BLEU and ROUGE for overlap, METEOR for semantic similarity, and BERTScore for embedding-based evaluation.

How does the regression detection work?

It compares new evaluation results against a saved baseline. If scores for key metrics drop beyond a defined threshold, it flags the change as a regression to prevent low-quality updates from reaching production.

Can this skill help evaluate RAG systems?

Yes, it includes specific metrics for Retrieval-Augmented Generation such as Mean Reciprocal Rank (MRR), NDCG, and custom groundedness checks to ensure answers are based on provided context.

What is 'LLM-as-judge' and how is it used?

LLM-as-judge is a pattern where a highly capable model (like Claude 3.5 Sonnet) is used to score or compare the outputs of other models based on specific rubrics like helpfulness, safety, or accuracy.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: Activer007

byActiver007

Data Science & ML

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

About

The LLM Evaluation skill provides a comprehensive toolkit for assessing the quality, accuracy, and reliability of Large Language Model outputs. It enables developers to move beyond vibes-based testing by establishing robust evaluation frameworks that combine automated NLP metrics like BLEU and BERTScore with sophisticated LLM-as-judge patterns. Whether you are validating RAG systems, comparing different model providers, or performing A/B tests on prompt iterations, this skill helps you detect regressions, measure groundedness, and build production-ready AI applications with statistical confidence.

Key Features

Statistical A/B testing framework with Cohen's d effect size
Specialized RAG metrics for retrieval quality and groundedness
LLM-as-judge patterns for pointwise and pairwise semantic evaluation
Automated regression detection to prevent performance degradation
0 GitHub stars
Automated text metrics including BLEU, ROUGE, and BERTScore

Use Cases

Comparing model performance across different versions or providers
Detecting performance regressions before deploying new prompt templates
Validating RAG system accuracy and document retrieval relevance

About

Key Features

Statistical A/B testing framework with Cohen's d effect size
Specialized RAG metrics for retrieval quality and groundedness
LLM-as-judge patterns for pointwise and pairwise semantic evaluation
Automated regression detection to prevent performance degradation
0 GitHub stars
Automated text metrics including BLEU, ROUGE, and BERTScore

Use Cases

Comparing model performance across different versions or providers
Detecting performance regressions before deploying new prompt templates
Validating RAG system accuracy and document retrieval relevance