Evaluation of RAG AI System for Diabetes Management

The document provides an evaluation of the RAG AI system's quality in managing diabetes. The system generates Q&A pairs covering diabetes management, which are reviewed for clinical accuracy, specificity, and practical utility. The feedback highlights the system's strengths in providing actionable medical advice and identifies areas for improvement, such as context sensitivity and clarity of individualized recommendations.

The overall assessment indicates that the RAG AI system produces clinically sound and referenced responses, effectively giving actionable advice in clear clinical scenarios. However, it sometimes provides generic answers for more complex questions involving overlapping diabetes types or specific individual cases. The system reliably cites authoritative guidelines and provides next steps, emphasizing the need for increased individualization and explicitness when patient-specific context is available.

The document also includes specific Q&A examples, such as the management of LADA (Latent Autoimmune Diabetes in Adults) and the adjustment of treatment for a patient with type 2 diabetes on Glucophage. Feedback from the Clinique du diabète is incorporated, noting the strengths and areas for improvement in the AI system's responses.