The relevance of low-code / no-code development has grown substantially in research and practice over the years to allow non- technical users to create applications and, therefore, democratise software development. One problem in this domain still persists: many platforms remain low-code as the underlying modeling layer still requires professionals to write/design a model, often using Domain Specific Languages (DSLs). With the rise of generative AI and Large Language Models (LLMs) and their capabilities, new possibilities emerge on how Low Code Development Platforms (LCDPs) can be improved. This paper shows how the capabilities of LLMs can be leveraged to turn DSL-based low-code platforms into true no-code. We an- alyzed how textual modeling can be replaced by generating the required model using LLMs. We conducted a user experiment to compare textual modeling with the use of LLMs for that task. Our results show that task completion time could be significantly im- proved, and the majority of users prefer using the LLM-aided mod- eling. Based on these findings, we discuss the integration of these techniques into an existing low-code platform to transform it into true no-code.