Chess is a deep and intricate game, the master of which depends on learning tens of thousands of the patterns that may occur on the board. At Noctie, their mission is to aid this learning process through humanlike chess AI. A prominent challenge lies in curating instructive chess positions for students. Usually these are either manually found by going through large numbers of real games, or handcrafted – a time-consuming process. For effective learning, it is often useful to collect many positions following the same theme, or exhibiting the same type of pattern. Curating such collections from real games is a challenging task. This thesis investigates the transformer decoder-only architecture and its capability of generating realistic, game-like chess-positions. This investigation involved the development and training of a decoder model using Pytorch, and a simple web-based Turing test gaining larger understanding of testers experience. The developed chess model successfully generates chess positions, with constraining possibilities of fixed pieces, score intervals, and fixed empty positions. Controlled re-generation ensures satisfaction of score intervals, while empty positions are handled by iterating over the model's probabilities. Based on the limited data provided by the Turing test, the model seems to fool players below 2000 rank-points on chess.com, where guess percentages land near the 50 percent mark, providing no clear indication that it deviates from randomness.