This research investigates the integration of ChatGPT, a generative pre-trained transformer model, within library systems to augment user experience and information retrieval efficacy. The study explores user interactions with ChatGPT, analyzes data security anxieties, assesses technical feasibility, and evaluates the influence of integration on users’ ability to find relevant resources. Findings reveal a significant portion of library patrons utilize chatbots for information retrieval, perceive ChatGPT as helpful for queries, and value personalized recommendations. Nevertheless, concerns regarding data privacy and the potential for diminished human interaction necessitate mitigation strategies. The study advocates for a measured approach to Generative Pre-trained Transformer (GPT) integration, emphasizing user training and transparent communication regarding data privacy practices. Furthermore, librarians should retain an active role to address complex inquiries demanding human expertise and attenuate potential biases within GPT responses.