Comparison
Answer Relevance vs Faithfulness
Answer Relevance and Faithfulness are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Answer Relevance
Answer Relevance comes up when the question is fundamentally about evaluation.
A user asks "what's the cancellation policy?" and the model returns the refund policy: faithful but low answer-relevance.
When you would reach for Faithfulness
Always for RAG — faithfulness is the single most actionable production metric.
Faithfulness eval flags an answer that cited "California enacted X in 2024" when the retrieved policy said 2023; the trace surfaces the original failure.
Frequently asked
What is the difference between Answer Relevance and Faithfulness?
Answer Relevance: Answer relevance measures whether the response actually answers the question asked — independent of whether it is true. The complement to faithfulness in RAG eval. Faithfulness: Faithfulness measures whether an LLM's answer is supported by the retrieved context — every claim either appears in the source material or follows directly from it. The most important RAG quality metric.
When should I use Answer Relevance vs Faithfulness?
Answer Relevance is the right concept when you are focused on evaluation. Always for RAG — faithfulness is the single most actionable production metric.
Are Answer Relevance and Faithfulness the same thing?
No. Answer Relevance is evaluation; Faithfulness is evaluation. They are related but address different parts of the AI stack.