ChatGPT is a large language model that can generate creative and on-topic responses, often providing details on why it generates a specific response. It has been asked to generate everything from answers to questions on soil physics to writing folk songs about beer! The model can remember its previous responses to have a (mostly) coherent conversation. One of the most interesting features of ChatGPT is that it can generate properly formatted and relevant code based on a simple natural language request, including Selenium in multiple languages. However, being able to write accurate code is just the beginning, as ChatGPT currently cannot provide perfect, executable code that needs no modifications. Nevertheless, it can still do quite impressive things. ChatGPT has the potential to be a low-code solution for automated testing, allowing users to write test cases in natural language and then use Cucumber to create a more structured test automation framework. The model can generate Cucumber code, which is beneficial because Cucumber scenarios combine the natural language intent of a test with the automation code that implements it. This makes it easier for testers who are not as familiar with test code to understand the linkage between test intent and test code. ChatGPT has been demonstrated to be able to write test scripts that can run on Sauce Labs, which is useful because running tests on Sauce Labs requires updating how the test is launched using Selenium. However, ChatGPT still has some issues, such as requiring users to have a reasonable understanding of the app under test and the coding language being used, and not being able to detect deprecated methods or accurately reflect what is being tested in the script. Overall, ChatGPT is a powerful natural language model with enormous potential for low-code testing solutions, but it still has limitations that need to be addressed.