What types of metrics does this skill provide?

The skill supports quantitative metrics like Exact Match and Token Overlap (Precision, Recall, F1), as well as qualitative metrics like Semantic Similarity and custom LLM-based scoring.

Can I run A/B tests with this skill?

Yes, it includes a dedicated A/B testing framework that allows you to define model variants, distribute traffic, and compare their performance results side-by-side.

How does the LLM-as-judge pattern work?

It uses a powerful language model to evaluate the output of another model based on defined criteria like accuracy, relevance, and clarity, providing both a numerical score and an explanation.

Does it support data validation for test sets?

Absolutely. It uses Pydantic models to ensure evaluation datasets are structured correctly with inputs, expected outputs, and metadata for reproducible testing.

LLM Evaluation Metrics

Name: LLM Evaluation Metrics
Author: ricardoroche

byricardoroche

Ciencia de Datos y ML

Standardizes LLM performance evaluation through automated datasets, A/B testing, and advanced LLM-as-judge patterns.

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The LLM Evaluation Metrics skill provides a rigorous framework for assessing AI model performance across diverse tasks. It enables developers to transition from anecdotal testing to data-driven optimization by providing structured patterns for evaluation datasets, quantitative metrics (like F1 and semantic similarity), and qualitative 'LLM-as-judge' scoring. With built-in support for A/B testing and experiment tracking, this skill ensures that model updates are validated against ground-truth references and specific performance benchmarks before deployment.

Características Principales

Structured evaluation dataset management with Pydantic validation
Comprehensive metric suite including Exact Match, Token Overlap, and Semantic Similarity
Automated LLM-as-judge implementation for qualitative scoring and reasoning
Async batch processing for efficient large-scale experiment tracking
Robust A/B testing framework to compare model variants and traffic weights
0 GitHub stars

Casos de Uso

Automating the quality assessment of long-form text generation using an LLM judge
Benchmarking new prompt versions against a golden test set to prevent regressions
Comparing the accuracy and latency of different model providers using A/B testing

Acerca de

Características Principales

Structured evaluation dataset management with Pydantic validation
Comprehensive metric suite including Exact Match, Token Overlap, and Semantic Similarity
Automated LLM-as-judge implementation for qualitative scoring and reasoning
Async batch processing for efficient large-scale experiment tracking
Robust A/B testing framework to compare model variants and traffic weights
0 GitHub stars

Casos de Uso

Automating the quality assessment of long-form text generation using an LLM judge
Benchmarking new prompt versions against a golden test set to prevent regressions
Comparing the accuracy and latency of different model providers using A/B testing