Atlas · GenAI 2026
RAG Evaluation
RAG evaluation (faithfulness, relevance, RAGAS)
conceptPeak: 2024RAG EvaluationAI consensus: 1/3
Prerequisites
You cannot evaluate a RAG system without understanding its components (retrieval quality, generation faithfulness, grounding)
- mediumModel Evaluation
RAG evaluation uses metrics concepts (precision@k, recall, F1) adapted to retrieval+generation context
Recommended reference
RAGAS docs: docs.ragas.io — the standard RAG eval framework; plus Es et al. (2024) 'RAGAS: Automated Evaluation of RAG' paper
Notes from AI deep research
Anthropic Opus
RAGAS: faithfulness, relevance, similarity. Bez mierzenia jakosci RAG nie da sie iterowac
OpenAI Deep Research
'Reference-free' ocena [OA#36]
Related skills
- → is part of: Retrieval-Augmented Generation(3/3)
- → is subcategory of: LLM Evaluation Design(3/3)
- → is part of: LLM Evaluation Frameworks(2/3)