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monitor-rag-quality

Evaluate RAG pipelines using context precision, recall, faithfulness, and answer relevance.

29|8|Updated Jul 5, 2026
Search Tags:#context retrieval#RAG#AI evaluation#quality assessment#answer generation
One-click install
npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill monitor-rag-quality
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: monitor-rag-quality
Source: https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/monitor-rag-quality
Command: npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill monitor-rag-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps diagnose issues in RAG (retrieval-augmented generation) pipelines, ensuring the correct context is retrieved and answers are faithful.

Core Features & Use Cases

  • Quality Measurement: Evaluate the effectiveness of RAG pipelines using four key metrics: context precision, recall, faithfulness, and answer relevance.
  • Diagnosis: Differentiate between retrieval and generation issues to apply appropriate fixes.
  • Use Case: When encountering incorrect RAG answers, this Skill can help identify whether the problem lies in the retrieval or generation process.

Quick Start

Use the monitor-rag-quality skill to evaluate the quality of your RAG pipeline by providing a set of examples with questions, expected answers, and retrieved context.

Frequently Asked Questions about monitor-rag-quality

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

FAQPage Schema
How do I evaluate RAG pipeline quality to fix incorrect answers?▼

To evaluate RAG pipeline quality, measure context precision, recall, faithfulness, and answer relevance using a set of example questions, expected answers, and retrieved contexts. This identifies whether incorrect answers stem from retrieval or generation failures.

What is the best way to diagnose retrieval vs generation issues in RAG?▼

Diagnosing retrieval vs generation issues in RAG requires measuring context precision and recall against retrieved documents, then checking answer faithfulness and relevance to differentiate retrieval failures from generation hallucinations.

How do I measure context precision and recall for AI answer generation?▼

Measure context precision and recall by providing example questions alongside their retrieved contexts and expected answers. This validates whether the retrieval mechanism fetches the exact information needed for accurate AI answer generation.

Why does my RAG system return unfaithful answers despite retrieving context?▼

RAG systems return unfaithful answers despite retrieving context when generation ignores the provided documents. Evaluating answer faithfulness and relevance metrics isolates this generation issue from underlying context retrieval failures.

Can I assess RAG faithfulness and answer relevance without external dependencies?▼

Yes, you can assess RAG faithfulness and answer relevance using internal scripts and references. Provide examples with questions, expected answers, and retrieved context to evaluate pipeline accuracy without requiring external dependencies.

Do I need expected answers to measure context precision in my RAG pipeline?▼

Yes, measuring context precision in a RAG pipeline requires expected answers alongside questions and retrieved contexts. These examples establish the baseline needed to determine if your retrieval process fetches the correct information.

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