Most artificial intelligence agents in interactive fiction games are implemented using reinforcement learning. Considering the recent rapid development of large language models, we propose an approach that utilizes a large language model to tackle interactive fiction game tasks. The chosen test dataset is TextWorld Commonsense, an interactive fiction game environment designed for artificial intelligence agents. In these games, the AI agent’s task is to organize rooms and place items in appropriate locations. To achieve a high score in the game, common sense knowledge about “which items belong to which locations” is important. Our approach is based on GPT-4 and a carefully designed prompt. Experimental results demonstrate that our approach outperforms prior research. Specifically, GPT-4 with feedback-augmented prompt successfully completed all tasks in both simple and medium level game environments without fine-tuning. In hard level game environments, our approach achieved a normalized score of 0.70, surpassing the best baseline score of 0.57.