Question

A Generative AI Engineer runs Mosaic AI Agent Evaluation on a RAG chatbot. The aggregate scores show high groundedness but low answer relevance. The team wants to understand whether the problem originates in retrieval or in generation before making changes. Within the Agent Evaluation results, which artifact should they inspect FIRST to pinpoint the source of the low relevance?

Accepted Answer

The per-request detailed trace, which shows the retrieved chunks alongside the generated response and the individual judge assessments for each evaluated row

Answer

The overall aggregate metric summary table averaged across the entire evaluation dataset

Answer

The model serving endpoint's inference table payload logs from the last 24 hours of production traffic

Answer

The Lakehouse Monitoring drift dashboard comparing the current scoring window to the baseline table