  Large Language Models (LLMs) have proven to be useful tools in various
domains outside of the field of their inception, which was natural language
processing. In this study, we provide practical directions on how to use LLMs
to generate 2D-game rooms for an under-development game, named Metavoidal. Our
technique can harness the power of GPT-3 by Human-in-the-loop fine-tuning which
allows our method to create 37% Playable-Novel levels from as scarce data as
only 60 hand-designed rooms under a scenario of the non-trivial game, with
respect to (Procedural Content Generation) PCG, that has a good amount of local
and global constraints.
