軟體設計審查（Design Review）是確保架構品質與系統可維護性的關鍵環節，然實務上多依賴於開發者經驗與主觀判斷，缺乏一致性與標準化。隨著大型語言模型（LLMs）於程式開發輔助中的應用興盛，已有研究嘗試以其協助程式品質分析，惟多聚焦於函式層級的程式碼異味（code smell），對架構層級設計審查，特別是在需求擴充情境下的適應性分析與優化建議生成，仍所涉不多。 本研究即以變更導向之架構層級設計審查為應用場景，評估 LLM 結合檢索增強生成技術（RAG），在審查既有系統架構應對新需求的能力時，是否能產出具品質的重構建議。實驗設計三種不同推理能力的語言模型與三種推理流程架構進行交叉測試。結果顯示， RAG 技術則能顯著提升低推理能力模型的生成品質，對中等推理能力模型亦有助於補足知識落差，促使其產出更具延伸性與結構性的重構建議。 本研究驗證了 RAG 技術於架構層級設計建議任務中的應用潛力，亦補足現有研究多聚焦於語法層級問題的侷限，並提出一套結合 LLM 與 RAG 的可行設計審查流程架構，為軟體開發流程的智慧化與自動化奠定基礎。;Software design review is essential for ensuring architectural quality and system maintainability. However, in practice, it often relies on developers’ experience and subjective judgment, lacking consistency and standardization. With the growing application of large language models (LLMs) in software development, existing studies have explored their use in code quality analysis, yet primarily focus on function-level code smells. Little attention has been paid to architecture-level review, especially in adaptive scenarios involving evolving requirements. This study investigates whether LLMs, combined with Retrieval-Augmented Generation (RAG), can produce high-quality refactoring suggestions for adapting system architectures to new demands. A cross-evaluation was conducted using three LLMs with different reasoning capabilities and three prompting workflows. Results show that RAG significantly improves output quality for lower-capacity models and helps medium-capacity models bridge knowledge gaps, enabling more structured and extensible recommendations. The findings validate the potential of RAG in architecture-level design review and address the limitations of prior research that focus mainly on syntax-level issues. A practical LLM+RAG-based design review workflow is proposed, laying the groundwork for intelligent and automated support in software architecture evaluation.
