  Two fundamental challenges face generative models in engineering
applications: the acquisition of high-performing, diverse datasets, and the
adherence to precise constraints in generated designs. We propose a novel
approach combining optimization, constraint satisfaction, and language models
to tackle these challenges in architectural design. Our method uses
Quality-Diversity (QD) to generate a diverse, high-performing dataset. We then
fine-tune a language model with this dataset to generate high-level designs.
These designs are then refined into detailed, constraint-compliant layouts
using the Wave Function Collapse algorithm. Our system demonstrates reliable
adherence to textual guidance, enabling the generation of layouts with targeted
architectural and performance features. Crucially, our results indicate that
data synthesized through the evolutionary search of QD not only improves
overall model performance but is essential for the model's ability to closely
adhere to textual guidance. This improvement underscores the pivotal role
evolutionary computation can play in creating the datasets key to training
generative models for design. Web article at https://tilegpt.github.io
