Psychological defense detection is one of essential present-day challenges in clinical practice. The state-of-the-art natural language processing (NLP) tools aim to automate this task. However, their potential and efficiency remain largely unexplored. This manuscript attempts to address this problem from various perspectives: it first explores the efficiency of direct large language model (LLM)-prompting. Then, it applies NLP techniques for LLM fine-tuning applied to the psychological defense classification task. Finally, it attempts to generate states of mind based on the speaker’s psychological state. The results show that the complexity of the task requires further improvement of the software solutions used.