Can this skill help with AI hallucinations?

Yes, it specifically provides 'Faithfulness' metric workflows to verify if an LLM's answer is supported by the retrieved context, effectively flagging hallucinations.

Is this suitable for production regression testing?

Absolutely. It provides patterns for unit testing LLM outputs to ensure that changes to prompts or models do not break existing performance standards.

How does LLM-as-a-judge work?

It uses a high-performance model (like GPT-4o) to grade the outputs of other models based on specific, research-backed criteria like professionalism or relevance.

Does this require manual data labeling?

No, the skill focuses on synthetic data generation and automated metrics to allow for high-scale testing without manual human intervention.

What frameworks are supported by this skill?

This skill includes implementation patterns and best practices for popular frameworks like Ragas, DeepEval, and LlamaIndex.

LLM & RAG Evaluation Frameworks

Name: LLM & RAG Evaluation Frameworks
Author: cuba6112

bycuba6112

Ciencia de Datos y ML

Implements robust evaluation patterns for RAG pipelines and LLM outputs using metrics like faithfulness, relevance, and synthetic data generation.

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This skill equips Claude with advanced patterns for benchmarking LLM applications, moving beyond traditional software metrics to objective, LLM-driven evaluation. It provides standardized workflows for Ragas, DeepEval, and LlamaIndex to measure faithfulness, detect hallucinations, and scale testing through synthetic dataset generation. By implementing 'LLM-as-a-judge' patterns, it helps developers ensure their AI outputs remain accurate, professional, and grounded in provided context during model upgrades or prompt iterations.

Características Principales

LLM-as-a-judge implementation using high-reasoning models for grading
Automated synthetic data generation for high-scale regression testing
RAG benchmarking with Faithfulness and Retrieval Relevance metrics
Unit testing patterns for LLM outputs using DeepEval and GEval
0 GitHub stars
Regression testing workflows to prevent quality degradation during updates

Casos de Uso

Scaling evaluation sets when manual gold-standard labels are unavailable
Comparing performance between different LLM versions or prompt iterations
Detecting and mitigating hallucinations in RAG-based applications

Acerca de

Características Principales

LLM-as-a-judge implementation using high-reasoning models for grading
Automated synthetic data generation for high-scale regression testing
RAG benchmarking with Faithfulness and Retrieval Relevance metrics
Unit testing patterns for LLM outputs using DeepEval and GEval
0 GitHub stars
Regression testing workflows to prevent quality degradation during updates

Casos de Uso

Scaling evaluation sets when manual gold-standard labels are unavailable
Comparing performance between different LLM versions or prompt iterations
Detecting and mitigating hallucinations in RAG-based applications