What is the LLM-as-judge pattern?

It is a technique where a secondary, usually smaller or more specialized LLM, is used to evaluate the outputs of a primary LLM against specific criteria like relevance, tone, or accuracy.

What is a quality gate in AI development?

A quality gate is a programmatic threshold that checks an AI's output against pre-defined scores; if the output doesn't meet the minimum threshold, it is blocked or rerouted for self-correction.

Can I use the same model to evaluate its own output?

No, it is a recommended best practice to use a different judge model (such as GPT-4o-mini or Claude Haiku) to evaluate another model's output to avoid self-bias and ensure objective scoring.

How does this skill help with RAG systems?

It includes built-in support for RAGAS metrics, allowing you to measure faithfulness, context precision, and answer relevancy to ensure your retrieval-augmented generation system is accurately grounded.

Does this skill support batch testing?

Yes, the skill includes capabilities for running evaluation suites across large datasets to generate performance reports and benchmark different model versions or prompt iterations.

LLM Output Evaluation

Name: LLM Output Evaluation
Author: yonatangross

byyonatangross

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データサイエンスとML

Evaluates AI-generated content quality using LLM-as-judge patterns, RAGAS metrics, and automated hallucination detection.

概要

This skill provides a comprehensive framework for assessing LLM outputs, ensuring production readiness through standardized quality gates and automated assessment pipelines. It implements industry-standard evaluation patterns like LLM-as-judge using cost-effective models, RAGAS metrics for RAG system validation, and sophisticated hallucination detection. By integrating these tools directly into your development workflow, you can automate quality assurance, run batch evaluations over golden datasets, and maintain high standards for AI-driven features.

主な機能

Automated LLM-as-judge patterns for multi-dimensional quality scoring
Batch evaluation and pairwise comparison for model performance benchmarking
Real-time hallucination detection and factual grounding checks
Standardized RAGAS metrics for RAG system validation (Faithfulness, Relevancy, Precision)
29 GitHub stars
Multi-metric quality gates to prevent low-quality content from reaching production

ユースケース

Implementing a 'Judge' layer to score and filter AI responses before user delivery
Benchmarking different LLM prompts or models using pairwise preference testing
Building automated CI/CD pipelines to validate RAG application performance

概要

主な機能

Automated LLM-as-judge patterns for multi-dimensional quality scoring
Batch evaluation and pairwise comparison for model performance benchmarking
Real-time hallucination detection and factual grounding checks
Standardized RAGAS metrics for RAG system validation (Faithfulness, Relevancy, Precision)
29 GitHub stars
Multi-metric quality gates to prevent low-quality content from reaching production

ユースケース

Implementing a 'Judge' layer to score and filter AI responses before user delivery
Benchmarking different LLM prompts or models using pairwise preference testing
Building automated CI/CD pipelines to validate RAG application performance