Retrieval-Augmented Generation for Precedent-Based Support in Early-Stage Architectural Design
Early-stage architectural design is highly dependent on precedent cases and accumulated domain knowledge, which help designers explore concepts, establish design logic, and reduce uncertainty. However, existing digital assistance tools struggle to effectively support this phase due to the dominance of visual information and the linguistic diversity found in architectural descriptions. This study addresses these challenges by proposing a retrieval-augmented generation (RAG) framework specifically tailored to architectural design contexts. Challenges in Precedent-Based Architectural Assistance Architectural precedents are complex, multimodal, and context-sensitive, combining drawings, images, diagrams, and textual narratives. Traditional retrieval systems often fail to capture the underlying design logic or to align visual data with semantic descriptions. These limitations reduce retrieval accuracy and restrict the usefulness of precedent recommendations during conceptual desig...