Does this skill help with production regressions?

Yes, it includes a RegressionDetector framework that compares new results against established baselines to flag significant performance drops before they reach users.

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

Yes, it includes specific strategies for measuring retrieval performance, coverage, and checking groundedness to ensure the model stays within the provided context.

How does the LLM-as-judge feature work?

It provides implementation patterns where a more capable model (like Claude 3.5 Sonnet or GPT-4o) evaluates a smaller or specialized model's output based on custom criteria like accuracy and clarity.

What metrics does this skill support?

It supports automated metrics like BLEU and ROUGE for text overlap, semantic metrics like BERTScore, retrieval metrics like MRR and NDCG, and custom qualitative metrics via LLM-as-judge patterns.

LLM Application Evaluation

Name: LLM Application Evaluation
Author: EricGrill

byEricGrill

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Ciencia de Datos y ML

Implements robust evaluation frameworks for AI applications using automated metrics, human feedback, and LLM-as-judge patterns.

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This skill provides a comprehensive toolkit for assessing the performance and reliability of Large Language Model (LLM) applications. It enables developers to implement standardized evaluation strategies ranging from traditional NLP metrics like BLEU and ROUGE to modern LLM-as-judge patterns and human-in-the-loop annotation workflows. By establishing consistent baselines, detecting performance regressions, and facilitating statistical A/B testing, this skill helps teams build confidence in their production AI systems and systematically validate improvements to prompts, models, and retrieval strategies.

Características Principales

LLM-as-Judge patterns for qualitative and pairwise assessments
RAG-specific evaluation metrics like MRR, NDCG, and groundedness
Statistical A/B testing framework with significance and effect size
2 GitHub stars
Automated NLP metrics including BLEU, ROUGE, and BERTScore
Regression detection to prevent performance degradation

Casos de Uso

Validating prompt engineering changes before production deployment
Measuring the accuracy and retrieval quality of RAG-based systems
Comparing performance and cost-efficiency across different LLM models

Acerca de

Características Principales

LLM-as-Judge patterns for qualitative and pairwise assessments
RAG-specific evaluation metrics like MRR, NDCG, and groundedness
Statistical A/B testing framework with significance and effect size
2 GitHub stars
Automated NLP metrics including BLEU, ROUGE, and BERTScore
Regression detection to prevent performance degradation

Casos de Uso

Validating prompt engineering changes before production deployment
Measuring the accuracy and retrieval quality of RAG-based systems
Comparing performance and cost-efficiency across different LLM models