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

データサイエンスとML

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

概要

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.

主な機能

LLM-as-Judge patterns for automated qualitative assessment
RAG-specific evaluation for retrieval (MRR, NDCG) and groundedness
0 GitHub stars
Statistical A/B testing framework with Cohen's d effect size
Automated regression detection to prevent quality drops during deployment
Automated NLP metrics including BLEU, ROUGE, and BERTScore

ユースケース

Comparing performance across different models or prompt versions
Validating RAG system accuracy against a gold-standard dataset
Establishing automated quality gates in CI/CD pipelines for AI apps

概要

主な機能

LLM-as-Judge patterns for automated qualitative assessment
RAG-specific evaluation for retrieval (MRR, NDCG) and groundedness
0 GitHub stars
Statistical A/B testing framework with Cohen's d effect size
Automated regression detection to prevent quality drops during deployment
Automated NLP metrics including BLEU, ROUGE, and BERTScore

ユースケース

Comparing performance across different models or prompt versions
Validating RAG system accuracy against a gold-standard dataset
Establishing automated quality gates in CI/CD pipelines for AI apps