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Background: Post-surgical cardiovascular monitoring places a heavy information burden on clinical teams, requiring rapid synthesis of patient history, intraoperative data, monitoring streams, and surgical outcomes evidence. Existing clinical decision support systems handle this integration poorly, and most offer little visibility into their reasoning. We present a Retrieval-Augmented Generation (RAG) architecture designed specifically for this domain, with a focus on evidence traceability and practical workflow integration. Methods: We developed a three-layer RAG architecture comprising: a retrieval layer to create 768-dimensional representations of clinical scenarios; an augmentation layer employing context-aware filtering and machine learning algorithms to integrate patient-specific data with retrieved evidence; and a generative layer using fine-tuned language models to synthesise actionable clinical recommendations. Results: An evaluation framework is proposed to assess the technical performance and clinical applicability of RAG architecture. The evaluation methodology encompasses technical validation of system components, assessment of clinical workflow integration potential, and analysis of interpretability features essential for healthcare deployment. Conclusions: We describe a RAG architecture for post-surgical cardiovascular monitoring in which every recommendation is linked to retrievable source documents, making the reasoning visible and challengeable. A structured evaluation framework is proposed to guide the system towards clinical validation.