通用大语言模型技术发展迅速，但在应用中存在幻觉严重、专业性不强等问题，无法满足建筑工程项目的专业问答需求。相较于直接改进大模型内部技术架构，检索增强生成（retrieval-augmented generation, RAG）技术能够在大模型生成回答时，利用外部知识库提供更专业、精准的信息支持，具有操作简单、更新灵活、普适性强的特点，已成为提升大模型专业问答性能的关键技术之一。基于RAG技术构建了一套面向建筑工程管理专业知识的增强大语言模型，并通过建造师、注册建筑师执业资格考试真题进行了评估，结果表明该RAG增强大模型相较Qwen-plus通用大模型，答题得分率提升了17.67%；在此基础上，探讨了RAG增强大模型问答增强效果及其局限性，旨在推动RAG技术在建筑工程项目的深度应用，助力打造精通建筑工程项目专业知识的AI助手。

In spite of the rapid development of general large language model (LLM) technology, there are significant issues such as hallucination and lack of professionalism in its application. In this case, the large model's ability to meet the specialized Q & A needs of a construction project is limited. Compared with the direct improvement of the internal architecture of LLM, retrieval-augmented generation (RAG) can provide more professional and accurate information support from external knowledge bases when generating answers in LLM. Characterized by simple operation, flexible updating, and strong universality, RAG has become one of the key technologies to enhance the performance of large model professional Q & A. A set of augmented LLM for construction engineering management professional knowledge was constructed based on the RAG and evaluated by the real questions of the licensing examination for construction engineers and registered architects. The results show that the Q & A score rate of RAG large language model is improved by 17.67%, compared with the Qwen-plus general large model. On this basis, RAG large language model Q & A enhancement effect and its limitations are discussed, aiming to promote the in-depth application of RAG technology in construction engineering projects, and to help create AI assistants proficient in the specialized knowledge of construction engineering projects.