What is faithfulness in RAG evaluation?

Faithfulness measures whether the generated answer is fully grounded in the retrieved context, with no information added from the model's parametric memory or fabricated. It is computed by extracting claims from the answer and verifying each against the retrieved passages, typically using a language model as judge. Low faithfulness means the model is hallucinating even with context provided, a serious production issue. Faithfulness is often more important than answer correctness in RAG because the whole point is grounding answers in verifiable sources.