Integrating Generative AI into Climate-Aided Design: Methods, Tools, and Professional Implications

The study discusses incorporating Generative AI (GenAI) tools into the Climate-Aided Design (CADE) model, emphasising their ability to solve sustain-able design problems. Using a custom-developed chatbot with expertise in climate data analysis and architecture principles, the research explores ways that GenAI can assist architects in designing sustainable buildings right from the design onset. The research approach integrates text-to-image models with Large Language Models to assess architectural responses in varying climatic conditions through two case studies: context-aware Athene and context-less Bari. By comparing the approaches, the research discusses both the potential and limitations inherent in current GenAI tools in handling urban contextual data in conjunction with climatic information. The study detects a generational shift in the utilisation of digital instruments, in which the democratisation of GenAI technologies presents a challenge as well as an opportunity for both architectural practice and education. Though the tools enhance creativity and accessibility, analytic rigour should still be ensured. The study concludes that integrating CADE methodology into text-to-image pipelines can bridge the gap between instant visual appeal and performance-driven design, particularly for small to medium-sized offices and educational contexts.