What is the LLM-as-Judge approach?

This technique uses a highly capable model (like Claude 3.5 Sonnet) to evaluate the outputs of other models or prompts based on specific rubrics like accuracy, helpfulness, and tone.

Can I use this for RAG applications?

Yes, it includes specific metrics for Retrieval-Augmented Generation (RAG) such as Mean Reciprocal Rank (MRR), NDCG, and groundedness checks to ensure context adherence.

What metrics does this skill support for text generation?

It supports industry-standard metrics including BLEU for overlap, ROUGE for summarization quality, and BERTScore for semantic similarity using embeddings.

Does it help with statistical significance?

Yes, the skill includes an A/B testing framework that calculates T-tests, p-values, and Cohen's d to help you determine if a change is statistically better than the baseline.

LLM Evaluation Suite

Name: LLM Evaluation Suite
Author: Activer007

byActiver007

Ciencia de Datos y ML

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.

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This skill provides a robust framework for assessing the quality and performance of Large Language Model (LLM) applications through a multi-layered approach. It encompasses automated text generation metrics like BLEU and BERTScore, classification performance tracking, and specialized RAG evaluation tools for retrieval accuracy. Beyond basic metrics, it facilitates advanced 'LLM-as-Judge' patterns, human annotation workflows, and statistical A/B testing to ensure model improvements are both significant and reliable. It is an essential tool for developers looking to build production-grade AI systems with measurable quality standards.

Características Principales

Statistical A/B testing framework with p-value and effect size calculations
Automated metrics including BLEU, ROUGE, and BERTScore for text similarity
LLM-as-Judge patterns for automated pointwise and pairwise evaluations
Comprehensive RAG metrics for measuring retrieval (MRR, NDCG) and groundedness
Regression detection to identify performance drops during the development cycle
0 GitHub stars

Casos de Uso

Comparing the performance and cost-effectiveness of different foundation models
Establishing automated quality gates within CI/CD pipelines for AI applications
Validating prompt engineering changes to ensure they improve rather than degrade output

Acerca de

Características Principales

Statistical A/B testing framework with p-value and effect size calculations
Automated metrics including BLEU, ROUGE, and BERTScore for text similarity
LLM-as-Judge patterns for automated pointwise and pairwise evaluations
Comprehensive RAG metrics for measuring retrieval (MRR, NDCG) and groundedness
Regression detection to identify performance drops during the development cycle
0 GitHub stars

Casos de Uso

Comparing the performance and cost-effectiveness of different foundation models
Establishing automated quality gates within CI/CD pipelines for AI applications
Validating prompt engineering changes to ensure they improve rather than degrade output