Aiming at the core pain points in nuclear power engineering modification, such as low efficiency of knowledge retrieval, cumbersome document preparation processes, insufficient standardization, and difficulties in expert experience inheritance, this paper proposes and constructs an intelligent auxiliary design system based on Large Language Models (LLMs). The system adopts a hybrid storage strategy combining vector databases and knowledge graphs to build a high-quality vertical knowledge base for engineering modification, successfully transforming unstructured tacit knowledge scattered in historical documents into computable and retrievable structured intelligence. On this basis, the system establishes a dynamic multi-dimensional tag system and an intelligent evolution mechanism, and innovatively creates an intelligent auxiliary design engine based on dynamic questionnaire interaction. Integrating template guidance, questionnaire interaction, and Retrieval-Augmented Generation (RAG) technology, the engine realizes the automatic preparation and compliance verification of design documents. Application results indicate that the system effectively guides the sorting of design inputs, significantly improves design efficiency and quality, and reduces the risk of human error. Finally, the paper outlines the future integration with external drawing platforms, 3D design platforms, and computational analysis platforms, aiming to build an online collaborative design platform covering the full life cycle and achieve an intelligent closed-loop of the entire engineering modification process.