What automated metrics are supported by this skill?

The skill provides implementations for standard NLP metrics including BLEU, ROUGE, and METEOR, as well as advanced embedding-based metrics like BERTScore for measuring semantic similarity.

Does it support regression testing for AI models?

Absolutely. It includes a RegressionDetector framework designed to compare new model outputs against a baseline and flag statistically significant drops in performance.

Can I use this for RAG (Retrieval-Augmented Generation) apps?

Yes, the skill includes specialized patterns for RAG evaluation, including measuring groundedness (faithfulness to context) and retrieval metrics like MRR and NDCG.

How does the LLM-as-Judge pattern work?

It utilizes a high-capability model (such as Claude 3.5 Sonnet or GPT-4) to grade the outputs of other models based on defined rubrics like accuracy, helpfulness, and clarity.

LLM Evaluation Framework

Name: LLM Evaluation Framework
Author: Tahir-yamin

byTahir-yamin

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Ciencia de Datos y ML

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

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The LLM Evaluation skill provides a comprehensive toolkit for assessing the performance, quality, and reliability of Large Language Model applications. It equips developers with the methodologies needed to move beyond 'vibes-based' testing by calculating automated metrics like BERTScore and ROUGE, implementing LLM-as-judge patterns, and conducting rigorous A/B testing. Whether you are validating prompt engineering changes, benchmarking different foundation models, or building a RAG pipeline, this skill provides the statistical framework and code patterns required to establish production-grade quality baselines and detect performance regressions.

Características Principales

3 GitHub stars
Human evaluation frameworks with inter-rater agreement (Cohen's Kappa)
LLM-as-Judge patterns for pointwise and pairwise assessment
RAG-specific metrics for groundedness, retrieval, and factuality
Statistical A/B testing with t-tests and effect size calculations
Automated text generation metrics (BLEU, ROUGE, BERTScore)

Casos de Uso

Validating the impact of system prompt changes on application accuracy and safety
Establishing continuous integration (CI) tests to flag performance regressions before deployment
Comparing model performance and cost-efficiency when migrating between LLM providers

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Características Principales

3 GitHub stars
Human evaluation frameworks with inter-rater agreement (Cohen's Kappa)
LLM-as-Judge patterns for pointwise and pairwise assessment
RAG-specific metrics for groundedness, retrieval, and factuality
Statistical A/B testing with t-tests and effect size calculations
Automated text generation metrics (BLEU, ROUGE, BERTScore)

Casos de Uso

Validating the impact of system prompt changes on application accuracy and safety
Establishing continuous integration (CI) tests to flag performance regressions before deployment
Comparing model performance and cost-efficiency when migrating between LLM providers