Your team built a RAG-based customer support assistant on Azure AI Foundry. During pre-production evaluation, stakeholders complain that the assistant sometimes states facts that are not present in the retrieved documents, even when the answers sound plausible. You need to configure an evaluation run that specifically quantifies how often the model produces content not supported by its retrieved context. Which built-in evaluator should you prioritize?