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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00543 |
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| _version_ | 1866914015106564096 |
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| author | Duggempudi, Jayakrishna Gao, Lu Senouci, Ahmed Han, Zhe Zhang, Yunpeng |
| author_facet | Duggempudi, Jayakrishna Gao, Lu Senouci, Ahmed Han, Zhe Zhang, Yunpeng |
| contents | This paper presents the development of an AI-powered workflow that uses Large Language Models (LLMs) to assist in drafting schematic architectural floor plans from natural language prompts. The proposed system interprets textual input to automatically generate layout options including walls, doors, windows, and furniture arrangements. It combines prompt engineering, a furniture placement refinement algorithm, and Python scripting to produce spatially coherent draft plans compatible with design tools such as Autodesk Revit. A case study of a mid-sized residential layout demonstrates the approach's ability to generate functional and structured outputs with minimal manual effort. The workflow is designed for transparent replication, with all key prompt specifications documented to enable independent implementation by other researchers. In addition, the generated models preserve the full range of Revit-native parametric attributes required for direct integration into professional BIM processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00543 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs Duggempudi, Jayakrishna Gao, Lu Senouci, Ahmed Han, Zhe Zhang, Yunpeng Artificial Intelligence This paper presents the development of an AI-powered workflow that uses Large Language Models (LLMs) to assist in drafting schematic architectural floor plans from natural language prompts. The proposed system interprets textual input to automatically generate layout options including walls, doors, windows, and furniture arrangements. It combines prompt engineering, a furniture placement refinement algorithm, and Python scripting to produce spatially coherent draft plans compatible with design tools such as Autodesk Revit. A case study of a mid-sized residential layout demonstrates the approach's ability to generate functional and structured outputs with minimal manual effort. The workflow is designed for transparent replication, with all key prompt specifications documented to enable independent implementation by other researchers. In addition, the generated models preserve the full range of Revit-native parametric attributes required for direct integration into professional BIM processes. |
| title | Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.00543 |