Question

A Generative AI Engineer is using Mosaic AI Agent Evaluation to assess a deployed RAG chatbot for a healthcare knowledge base. Stakeholders complain that some answers sound confident but contain claims not supported by the retrieved documents. The engineer has a golden evaluation dataset with questions but no ground-truth answers. Which built-in LLM-as-judge metric should the engineer prioritize to catch these unsupported claims?

Accepted Answer

Groundedness — measures whether the response is factually supported by the retrieved context

Answer

Answer correctness — compares the response against a labeled ground-truth answer

Answer

Chunk relevance — measures whether each retrieved chunk is relevant to the question

Answer

Latency — measures the end-to-end response time of the agent