Research on the Application of Large Language Models with Modular Knowledge Enhancement in Automated Project Proposal Generation

With the development of artificial intelligence technology, large language models have shown tremendous potential in professional document generation. However, in document generation tasks with high professionalism and strict content requirements, such as project proposals, ensuring the accuracy and professionalism of generated content remains challenging. This paper proposes a project proposal generation method based on modular knowledge-enhanced large language models. First, this method divides the complex proposal generation task into interrelated submodules such as project background, technical approach, and implementation plan through modular decomposition, and designs a complete intermodule relationship processing mechanism. Second, it constructs a knowledge injection framework based on professional literature, providing professional domain knowledge support for the model through multi-level literature retrieval, knowledge extraction, and organization. Finally, it implements an automatic proposal generation system based on microservice architecture, integrating core functional modules such as literature processing, content generation, and quality control. Experimental results show that this method can significantly improve the professionalism and accuracy of proposal generation, providing new ideas and methods for AI-assisted professional document writing.