Implement RAG evaluation using the RAGAS framework, which measures four core metrics: Faithfulness (whether the answer is grounded in retrieved context), Answer Relevancy (whether the answer addresses the question), Context Precision (whether retrieved chunks are ranked by relevance), and Context Recall (whether the retrieved context contains all necessary information).
RAGAS (Retrieval Augmented Generation Assessment) is a comprehensive evaluation framework for RAG systems that operates without ground truth labels, instead using LLMs as evaluators to score performance. The framework measures four key metrics that address distinct failure modes: faithfulness detects whether the model added unsupported claims beyond the retrieved context, answer relevancy penalizes irrelevant or verbose responses, context precision ensures the highest-ranked documents are most relevant, and context recall checks for missing information that would lead to incomplete answers. RAGAS integrates with MLflow and can be used both as a Python library for offline evaluation and as real-time scorers in production pipelines.
Faithfulness: Measures whether the generated answer is factually consistent with the retrieved context, detecting hallucinations where the model adds unsupported claims.
Answer Relevancy: Evaluates whether the generated answer directly addresses the user's query, penalizing irrelevant or verbose responses.
Context Precision: Assesses whether the retrieved chunks are ranked correctly, with the most relevant documents appearing first.
Context Recall: Determines whether the retrieved context contains all necessary information to answer the query completely.