Lot-Sizing Problems are critical for operations management, with significant implications for production effectiveness and control. Traditional approaches, including heuristics and exact optimization methods, have long been used to address this challenge. However, recent advances in artificial intelligence, particularly in Large Language Models (LLMs) such as ChatGPT, offer a novel opportunity to approach such a complex operational problem. The potential for LLMs like ChatGPT to serve as accessible tools for small businesses is particularly compelling, as they can offer practical, user-friendly and do not require extensive technical expertise. This paper highlights the revolutionary potential of ChatGPT in production planning, marking a significant shift from conventional methods toward AI-driven decision-making, with the capacity to streamline lot-sizing processes and enhance operational efficiency. This study explores the application of ChatGPT, specifically the enhanced ChatGPT-4o model, to the Lot-Sizing Problem in a simplified setting involving a single product and no capacity constraints. Initial results show that ChatGPT-4o can generate solutions that are nearly optimal, often outperforming traditional heuristics like Silver Meal (SM), Parts Period Balancing (PPB), and Economic Order Quantity (EOQ). While ChatGPT demonstrates promising capabilities in this scenario, further work is needed to adapt the model for more complex situations, such as those involving multiple products and capacity limits.