This research explores the integration of generative artificial intelligence (AI) into early-stage architectural design through a creative assistant that translates natural language briefs into 3D volumetric apartment layouts. Existing AI research shows promising results in generating spatial configurations, but they fall short in supporting real-time interaction, spatial awareness, and editable outputs. To address this gap, a new methodology is proposed that integrates OpenAI’s GPT-4o model into Rhino and Grasshopper, allowing architects to co-create volumetric layouts through a conversational interaction. The assistant was incrementally fine-tuned on realistic architectural data from the RPLAN dataset to learn spatial logic, reasoning, geometry generation, and evaluation. A custom creativity evaluation metric was developed to test the model’s accuracy and novelty performance. Accuracy was measured by how well the output aligned with the given prompt. and novelty was measured by how original it was compared to the closest reference layout and the training dataset. A residential apartment design case study was conducted to test the full workflow from reasoning to generating and evaluating. During testing, the assistant showed the ability to iteratively respond to feedback without explicit training. This demonstrates the potential of “centauric” design workflows, where human intuition and machine intelligence collaborate in real time. The final model in the pipeline achieved an average novelty score of around 69%. This system contributes a new foundation for human-AI co-design, possibly enhancing design quality, adaptability, and efficiency in future architectural practice.