<![CDATA[Generative artificial intelligence (AI) is increasingly embedded in architectural practice, yet its contribution to off-grid autonomy remains uneven and insufficiently theorized. While AI tools are applied across stages ranging from conceptual visualization to performance optimization and operational energy management, their architectural impact varies depending on where they intervene within the design continuum. This study develops a typological framework that classifies generative AI applications according to design stage and subsystem integration depth. Through comparative analysis of precedent projects including Hy-Fi Pavilion, the NEST Project, Cal-Earth EcoDomes, Solar Decathlon prototypes, and AI-optimized renewable systems, the research evaluates how generative AI contributes to energy autonomy, water and waste integration, lifecycle strategy, and environmental validation. The findings indicate that diffusion-based systems primarily expand morphological exploration, whereas parametric–evolutionary and simulation-integrated generative frameworks demonstrate significantly greater potential for multi-system optimization in off-grid architecture. The study concludes that the effectiveness of generative AI in autonomous design is determined less by technological sophistication than by the degree to which it is embedded within validated environmental feedback loops.]]>
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