Comparison
Answer Relevance vs LLM-as-Judge
Answer Relevance and LLM-as-Judge 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 LLM-as-Judge
When you need to evaluate thousands of open-ended outputs cheaply and quickly.
MT-Bench: GPT-4 scoring 80 multi-turn questions.
Frequently asked
What is the difference between Answer Relevance and LLM-as-Judge?
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. LLM-as-Judge: LLM-as-judge uses a strong LLM to score or compare outputs from other LLMs. It is how most production teams evaluate quality at scale when human review is too slow.
When should I use Answer Relevance vs LLM-as-Judge?
Answer Relevance is the right concept when you are focused on evaluation. When you need to evaluate thousands of open-ended outputs cheaply and quickly.
Are Answer Relevance and LLM-as-Judge the same thing?
No. Answer Relevance is evaluation; LLM-as-Judge is evaluation. They are related but address different parts of the AI stack.