Question

A Generative AI Engineer is running Mosaic AI Agent Evaluation on a RAG-based customer support assistant. The evaluation dataset includes a column with expert-written reference answers for each question. The team wants to measure how factually and semantically close the agent's generated responses are to these reference answers, independent of whether the retrieved context supported them. Which built-in Agent Evaluation metric directly addresses this requirement?

Accepted Answer

answer correctness, which compares the agent's response against the provided ground-truth reference answers

Answer

groundedness, which checks whether the response is supported by the retrieved context

Answer

chunk relevance (retrieval precision), which measures how relevant retrieved documents are to the request

Answer

safety, which flags whether the response contains harmful or toxic content