LLM and Pattern Language Synthesis: A Hybrid Tool for Human-Centered Architectural Design

This paper introduces a hybrid design framework that combines Christopher Alexander’s Pattern Language with generative AI. Advanced large language models (LLMs) enable real-time synthesis of design patterns, making complex architectural choices accessible and comprehensible to stakeholders without specialized architectural knowledge. A lightweight, web-based tool lets project teams rapidly assemble context-specific subsets of Alexander’s 253 patterns, reducing a traditionally unwieldy 1,166-page corpus to a concise, shareable list. Demonstrated through a case study of a university department building, this method results in environments that are psychologically welcoming, fos-tering health, productivity, and emotional well-being. LLMs translate these curated patterns into vivid experiential narratives—complete with neuro-scientifically informed ornamentation. LLMs produce representative images from the verbal narrative, re-vealing a surprisingly traditional design that was never inputted as a prompt. Two separate LLMs (for cross-checking) then predict the pattern-generated design to catalyze improved productivity as compared to a standard campus building. By bridging abstract design principles and concrete human experience, this approach democratizes archi-tectural planning grounded on Alexander’s human-centered, participatory ethos.