Building on but moving far beyond the single-specialty focus of HandRAG, this session examines how Retrieval-Augmented Generation can be engineered to support clinical reasoning across multiple high stakes surgical areas, including orthopedic, cardiovascular, neurosurgical, and plastic surgery domains. Using a corpus of more than 7,800 clinical publications and cross specialty validation studies, the talk highlights practical methods for structuring heterogeneous medical data, optimizing vector retrieval with up to 35% latency gains, and designing prompts that preserve terminology accuracy across diverse subspecialties. Attendees will also learn a three-tier evaluation framework that improved critical-error detection by 2.4×, as well as deployment strategy such as automated literature refresh pipelines and cost-efficient architectures that reduced inference spending by 60% that enable RAG systems to operate reliably in real production healthcare settings.