Large Language Models (LLMs) have brought us efficient tools for various natural language processing (NLP) tasks. This paper explores the application of LLMs for extracting domain-specific terms from textual data. We will present the advantages and limitations of using LLMs for this task and will highlight the significant improvements they offer over traditional terminology extraction methods such as rule-based and statistical approaches.