Techniques for generative design based on large language models

One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:

• A computer-implemented method for generating training data for a large language model, the method comprising:
• receiving a plurality of design examples;
• evaluating each of the plurality of design examples using a plurality of performance metrics to generate corresponding design attributes for each of the plurality of design examples;
• storing the plurality of design examples in a design grid as initial candidate design layouts at a location in the design grid based on the corresponding design attributes;
• selecting one or more candidate design layouts from the design grid as one or more parent candidate design layouts;
• generating a new candidate design layout from the one or more parent candidate design layouts;
• evaluating the new candidate design layout using the plurality of performance metrics to generate new design attributes;
• storing the new candidate design layout in the design grid based on the new design attributes;
• and
• generating training data for a large language model based on the candidate design layouts in the design grid and the corresponding design attributes for the candidate design layouts.

A system comprising:

• one or more memories storing instructions, and
• one or more processors that are coupled to the one or more memories and,
• when executing the instructions, are configured to:
• receive a plurality of design examples;
• evaluate each of the plurality of design examples using a plurality of performance metrics to generate corresponding design attributes for each of the plurality of design examples;
• store the plurality of design examples in a design grid as initial candidate design layouts at a location in the design grid based on the corresponding design attributes;
• select one or more candidate design layouts from the design grid as one or more parent candidate design layouts;
• generate a new candidate design layout from one or more the parent candidate design layouts;
• evaluate the new candidate design layout using the plurality of performance metrics to generate new design attributes;
• store the new candidate design layout in the design grid based on the new design attributes;
• and
• generate training data for a large language model based on the candidate design layouts in the design grid and the corresponding design attributes for the candidate design layouts.