Imagine a world where writing test cases for complex industrial control systems is as simple as asking an AI. That's the promise of a new research paper exploring how Large Language Models (LLMs) can automate the tedious process of generating test cases for Programmable Logic Controllers (PLCs) and Distributed Control Systems (DCSs). Traditionally, testing this control logic has been a manual, error-prone affair, often left until the final stages of a project. This research introduces a novel approach using LLMs to create these test cases automatically. The process begins by feeding the LLM the control code, along with a template and any necessary supporting functions. The LLM then generates a series of test cases in a structured format, which are then converted into executable code. Initial experiments with open-source function blocks show promising results. The LLM-generated tests achieved high statement coverage for less complex programs, meaning they effectively exercised different parts of the code. However, the research also highlights current limitations. While LLMs excel at generating diverse test inputs, they sometimes struggle to predict the correct outputs, leading to erroneous assertions. This means human oversight is still crucial for validating the AI-generated tests. This research opens exciting possibilities for faster, more efficient testing in industrial automation. By automating test case creation, engineers can focus on higher-level design and problem-solving. However, further research is needed to refine the accuracy of LLM-generated assertions and extend the approach to more complex, time-dependent control systems. The future of testing may well involve a collaborative partnership between human ingenuity and the analytical power of AI.