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evaluation-metrics

Evaluate LLM outputs using BLEU, ROUGE, BERTScore, and RAG metrics.

2|1|Updated Nov 18, 2025
Search Tags:#rag#benchmarking#llm evaluation#metrics#hallucination detection#model quality
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-engineer --skill evaluation-metrics-pluginagentmarketplace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluation-metrics
Source: https://github.com/pluginagentmarketplace/custom-plugin-ai-engineer/tree/main/skills/evaluation-metrics
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-engineer --skill evaluation-metrics-pluginagentmarketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ragas, datasets, evaluate, langchain, tenacity, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the critical need to systematically measure and improve the quality of Large Language Models (LLMs), especially in production environments, by providing frameworks, benchmarks, and metrics.

Core Features & Use Cases

  • Comprehensive Metrics: Offers a suite of text generation and RAG-specific metrics (BLEU, ROUGE, BERTScore, Faithfulness, Relevancy, etc.).
  • Benchmark Suites: Integrates with standard benchmarks like MMLU and HumanEval for robust model assessment.
  • Evaluation Frameworks: Provides tools for structured evaluation, A/B testing, and hallucination detection.
  • Use Case: An AI engineer needs to compare two LLM models for a customer support chatbot. They can use this Skill to run an A/B test, evaluating metrics like faithfulness and answer relevancy on a set of test questions to determine which model performs better.

Quick Start

Use the evaluation-metrics skill to evaluate a list of model predictions against their ground truth references using BLEU and ROUGE scores.

Frequently Asked Questions about evaluation-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate LLM quality for a production chatbot?▼

To evaluate LLM quality for production chatbots, use A/B testing frameworks to compare model predictions against ground truth references. Measure text generation metrics like BLEU and ROUGE alongside RAG-specific metrics such as faithfulness and answer relevancy to determine which model performs better.

What is the best way to detect hallucinations in LLM outputs?▼

The best way to detect hallucinations in LLM outputs is to use dedicated hallucination detection tools within an LLM evaluation framework. These tools systematically measure the faithfulness of generated text against source context to identify ungrounded or fabricated information.

How do I measure RAG faithfulness and answer relevancy?▼

To measure RAG faithfulness and answer relevancy, use a RAG-specific evaluation framework like ragas. These frameworks assess whether generated answers are factually consistent with retrieved context and directly address the user's query.

Can I use standard benchmarks like MMLU and HumanEval for LLM evaluation?▼

Yes, you can use standard benchmark suites like MMLU and HumanEval for robust LLM evaluation. Integrating these benchmarks allows you to systematically assess model quality and compare performance across different models and tasks.

Does this evaluation framework support A/B testing for different LLM models?▼

Yes, this evaluation framework supports A/B testing for different LLM models. It provides structured tools to run comparative evaluations on test questions, allowing you to measure metrics like faithfulness and answer relevancy to determine which model performs better.

What text generation metrics are available for evaluating model predictions?▼

Available text generation metrics for evaluating model predictions include BLEU, ROUGE, and BERTScore. These metrics allow you to quantitatively compare a list of model predictions against their ground truth references to measure quality.

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