Your team runs an automated evaluation flow in Azure AI Foundry for a customer-support RAG chatbot. Stakeholders complain that some answers are factually consistent with the retrieved documents but do not actually address what the user asked. You need to add a built-in AI quality metric to the evaluation that specifically measures how well the generated answer addresses the user's original question, independent of whether the answer is supported by the retrieved context. Which metric should you configure?