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This guide provides a complete introduction to RAGAS (Retrieval-Augmented Generation Assessment): **Core Concepts:** - What is RAGAS and why it's needed for RAG system evaluation - Automated, quantifiable, and trackable quality assessment **Four Key Metrics Explained:** 1. Context Precision (0.7-1.0): How relevant are retrieved documents? 2. Context Recall (0.7-1.0): Are all key facts retrieved? 3. Faithfulness (0.7-1.0): Is the answer grounded in context (no hallucination)? 4. Answer Relevancy (0.7-1.0): Does the answer address the question? **How It Works:** - Uses evaluation LLM to judge answer quality - Workflow: test dataset → run RAG → RAGAS scores → optimization insights - Integrated with LightRAG's existing evaluation module **Practical Usage:** - Quick start guide for LightRAG users - Real output examples with interpretation - Cost analysis (~$1-2 per 100 questions with GPT-4o-mini) - Optimization strategies based on low-scoring metrics **Limitations & Best Practices:** - Depends on evaluation LLM quality - Requires high-quality ground truth answers - Recommended hybrid approach: RAGAS (scale) + human review (depth) - Decision matrix for when to use RAGAS vs alternatives **Use Cases:** ✅ Comparing different configurations/models ✅ A/B testing new features ✅ Continuous performance monitoring ❌ Single component evaluation (use Precision/Recall instead) Helps users understand and effectively use RAGAS for RAG system quality assurance. |
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| .. | ||
| Algorithm.md | ||
| DockerDeployment.md | ||
| EvaluatingEntityRelationQuality-zh.md | ||
| FrontendBuildGuide.md | ||
| LightRAG_concurrent_explain.md | ||
| OfflineDeployment.md | ||
| PerformanceFAQ-zh.md | ||
| PerformanceOptimization-zh.md | ||
| PerformanceOptimization.md | ||
| SelfHostedOptimization-zh.md | ||
| UV_LOCK_GUIDE.md | ||
| WhatIsGleaning-zh.md | ||
| WhatIsRAGAS-zh.md | ||