Best Practices in RAG Evaluation: A Comprehensive Guide - Qdrant

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This post provides a comprehensive guide to evaluating RAG systems, focusing on accuracy and quality. It details how to test for search precision, recall, contextual relevance, and response accuracy. It introduces and explains the use of Ragas, Quotient AI, and Arize Phoenix frameworks for RAG evaluation. The post also identifies common RAG underperformance issues, including improper data ingestion (emphasizing chunk size, overlap, and strategy), incorrect embedding (recommending MTEB benchmarks and FastEmbed), and unoptimized retrieval procedures (discussing Precision@k, MRR, and NDCG metrics).