Which metrics are best for evaluating RAG applications?

For RAG, you should focus on retrieval metrics like Mean Reciprocal Rank (MRR) and Hit Rate, alongside generation metrics like groundedness, context relevance, and BERTScore.

Can I automate regression testing for my AI model?

Yes, the skill includes a Regression Detector that compares new model outputs against a historical baseline and flags significant performance drops in key metrics.

What is the 'LLM-as-Judge' approach?

LLM-as-Judge is a technique where a highly capable language model is used to evaluate the outputs of other models based on a set of criteria like accuracy, helpfulness, and tone, providing scalable qualitative feedback.

How does this skill help with prompt engineering?

It provides a framework to run controlled experiments, allowing you to statistically compare the results of different prompts to ensure changes actually improve performance without causing regressions.

LLM Evaluation & Benchmarking

Name: LLM Evaluation & Benchmarking
Author: as4584

byas4584

0•

Ciencia de Datos y ML

Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human-in-the-loop feedback, and A/B testing.

The LLM Evaluation skill provides a robust toolkit for measuring and improving the quality of AI-driven applications. It enables developers to move beyond vibes-based testing by implementing standardized evaluation strategies, ranging from traditional NLP metrics like BLEU and ROUGE to modern LLM-as-judge patterns and statistical A/B testing. Whether you are optimizing RAG retrieval, comparing model performance, or establishing safety baselines, this skill provides the necessary patterns to ensure your LLM outputs are accurate, grounded, and production-ready.

Características Principales

01LLM-as-Judge patterns for automated qualitative assessment

02RAG-specific evaluation for retrieval (MRR, NDCG) and groundedness

030 GitHub stars

04Statistical A/B testing framework with Cohen's d effect size

05Automated regression detection to prevent quality drops during deployment

06Automated NLP metrics including BLEU, ROUGE, and BERTScore

Casos de Uso

01Comparing performance across different models or prompt versions

02Validating RAG system accuracy against a gold-standard dataset

03Establishing automated quality gates in CI/CD pipelines for AI apps

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add as4584/antigravity-skills llm-evaluation

For use in Claude.ai and ChatGPT

Download Skill