What metrics does this skill support for LLM testing?

It supports a wide range of metrics including linguistic overlap (BLEU, ROUGE), semantic similarity (BERTScore), and retrieval metrics (MRR, NDCG) for RAG systems.

Does it support human-in-the-loop evaluation?

Absolutely. It provides structures for human annotation tasks and includes tools to calculate inter-rater agreement using Cohen's Kappa score to ensure evaluation consistency.

How does the 'LLM-as-judge' pattern work?

This pattern uses a highly capable model (like Claude 3.5 Sonnet or GPT-4o) to evaluate the outputs of other models based on specific rubrics, providing both quantitative scores and qualitative reasoning.

Can I use this skill to prevent regressions in my AI app?

Yes, it includes a RegressionDetector that compares new results against a baseline and flags significant performance drops based on a configurable threshold.

LLM Evaluation & Testing

Name: LLM Evaluation & Testing
Author: Kingly-Agency

byKingly-Agency

数据科学与机器学习

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.

关于

This skill provides a robust framework for measuring the performance and quality of Large Language Model (LLM) applications. It enables developers to implement automated metrics like BLEU, ROUGE, and BERTScore, while also supporting advanced techniques such as LLM-as-judge, human annotation workflows, and statistical A/B testing. By establishing clear baselines and regression detection, it ensures that model updates or prompt changes improve system performance without introducing unexpected behaviors or quality degradations in production environments.

主要功能

LLM-as-judge patterns for pointwise and pairwise comparisons
Statistical A/B testing with Cohen's d effect size analysis
Automated text metrics including BLEU, ROUGE, and BERTScore
Human evaluation frameworks with inter-rater agreement calculation
RAG-specific metrics like MRR, NDCG, and groundedness checks
0 GitHub stars

使用场景

Validating prompt engineering improvements through systematic benchmarking
Comparing performance and cost-efficiency between different LLM providers
Detecting performance regressions in CI/CD pipelines before deployment

关于

主要功能

LLM-as-judge patterns for pointwise and pairwise comparisons
Statistical A/B testing with Cohen's d effect size analysis
Automated text metrics including BLEU, ROUGE, and BERTScore
Human evaluation frameworks with inter-rater agreement calculation
RAG-specific metrics like MRR, NDCG, and groundedness checks
0 GitHub stars

使用场景

Validating prompt engineering improvements through systematic benchmarking
Comparing performance and cost-efficiency between different LLM providers
Detecting performance regressions in CI/CD pipelines before deployment