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RAG Evaluation Skill

Evaluate RAG retrieval, generation, and latency metrics against defined targets.

Updated Aug 23, 2026
Search Tags:#llm#rag#metrics#evaluation#retrieval#generation#benchmark
One-click install
npx skills add https://github.com/davicqueiroz/claude-rag-skills --skill rag-evaluation-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: RAG Evaluation Skill
Source: https://github.com/davicqueiroz/claude-rag-skills/tree/main/rag-eval
Command: npx skills add https://github.com/davicqueiroz/claude-rag-skills --skill rag-evaluation-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you rigorously assess the quality of Retrieval-Augmented Generation (RAG) systems, ensuring they meet performance targets before deployment.

Core Features & Use Cases

  • Comprehensive Metrics: Evaluates retrieval, generation, and latency using standard benchmarks.
  • Flexible Modes: Supports local evaluation with custom datasets or benchmarking against a production API.
  • Use Case: A machine learning engineer needs to compare two different RAG pipeline configurations to determine which one provides more accurate and faster responses for customer support queries.

Quick Start

Run the rag-eval skill to evaluate your RAG system using the provided test dataset.

Frequently Asked Questions about RAG Evaluation Skill

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

FAQPage Schema
How do I evaluate RAG system quality before deployment?▼

Evaluating RAG system quality requires measuring retrieval, generation, and latency metrics against defined targets using standard benchmarks. This ensures the system meets performance requirements before deployment.

What metrics are used for RAG evaluation?▼

RAG evaluation metrics include retrieval accuracy, generation quality, and latency measurements. These metrics are compared against defined targets to assess overall system performance.

Can I benchmark my RAG pipeline against a production API?▼

Yes, you can benchmark your RAG pipeline against the Ailog production RAG API. This allows you to compare your local evaluation results with a production system.

What format should test datasets be for RAG evaluation?▼

Test datasets for RAG evaluation should include queries, expected answers, and relevant document identifiers. This format allows for comprehensive measurement of retrieval and generation accuracy.

How do I compare different RAG pipeline configurations?▼

Comparing RAG pipeline configurations involves running evaluations on both setups using the same test dataset. You can then analyze the retrieval, generation, and latency metrics to determine which configuration performs better.

Do I need to provide my own dataset for RAG evaluation?▼

Providing your own dataset for local RAG evaluation is required, containing queries, expected answers, and relevant document identifiers. Alternatively, you can benchmark against the Ailog production RAG API.

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