  Procedural content generation uses algorithmic techniques to create large
amounts of new content for games at much lower production costs. In newer
approaches, procedural content generation utilizes machine learning. However,
these methods usually require expensive collection of large amounts of data, as
well as the development and training of fairly complex learning models, which
can be both extremely time-consuming and expensive. The core of our research is
to explore whether we can lower the barrier to the use of personalized
procedural content generation through a more practical and generalizable
approach with large language models. Matching game content with player
preferences benefits both players, who enjoy the game more, and developers, who
increasingly depend on players enjoying the game before being able to monetize
it. Therefore, this paper presents a novel approach to achieving
personalization by using large language models to propose levels based on the
gameplay data continuously collected from individual players. We compared the
levels generated using our approach with levels generated with more traditional
procedural generation techniques. Our easily reproducible method has proven
viable in a production setting and outperformed levels generated by traditional
methods in the probability that a player will not quit the game mid-level.
