With improvements in technology, approaches, and methods, software engineering is al-ways changing to increase the effectiveness and methodology of software development processes. The crucial stage of requirement elicitation, which establishes the framework for software system design and implementation, is essential to this evolution. Conventional approaches to requirement elicitation frequently present difficulties like time commitment, in-consistent results, and oversight risk.

To oppose these challenges, this study evaluates OpenAI’s Chat Generative Pre-Trained Transformer (ChatGPT) tool’s viability and efficacy in automating the process of creating software requirements. Based on theoretical fundament, the study uses a case study approach to investigate ChatGPT's potential as a tool for requirement elicitation. Using both actual data and ideas from the literature, the research also looks at ChatGPT's understanding and processing of input.

The findings show that although ChatGPT offers a unique method for requirements documentation, its ap-plicability depends on the specifics and intricacy of the project in hand. Bigger project case showed more detailed and deep results, but also needed stronger background, while smaller projects showed the need for more specifications in case to be more accurate. Overall ChatGPT showed a great understanding of the topic and desires from project description alone, which shows the possibility to integrate AI, ChatGPT specific, into requirement elic-itation process. Most importantly, generation still requires a human verification regardless the project size.