Research on AI-assisted design method of substation based on LLM+RAG

In the digital age, in order to solve the problems of illusion, timeliness, data security, etc. of large language model technology (LLM), retrieval-augmented generation (RAG) has emerged as a feasible solution. Therefore, the architecture concept of LLM+RAG was proposed, in order to assist the design work as an efficient AI tool. Therefore, this paper adopts a layered design, and realizes the end-to-end process from professional knowledge acquisition to intelligent suggestion generation through six core functional layers: knowledge base construction layer, retrieval enhancement layer, context construction layer, LLM interaction layer, response optimization layer and interaction integration layer. In the design process, various language processing, retrieval, context model generation and other technologies are combined to complete a feasible RAG+LLM AI- assisted design architecture. Finally, through the real-time updated professional knowledge base, multi-way recall mechanism and chain reasoning method, the tool can provide reliable design suggestions for design work and improve the efficiency and quality of substation design. This study provides a design idea of a reliable RAG+LLM architecture, and finally completes the construction of an AI-assisted design tool using this architecture.