Researchers have developed a novel constrained natural-language interface designed to enhance finite element simulations within the FEniCS framework, specifically addressing the reliability concerns associated with large language models (LLMs) generating critical solver code. While LLMs offer potential for streamlining the setup of complex multi-physics simulations, their direct involvement in creating solver code introduces significant risks to accuracy and stability. This new interface circumvents these dangers by limiting the LLM's role to front-end operations1. The system employs the LLM solely for interpreting natural language prompts, converting them into structured JSON data, and generating geometric mesh definitions using Gmsh. Crucially, the LLM is prevented from interfering with or generating the core variational solver logic, which remains robustly defined. This approach leverages the efficiency of LLMs for preparatory tasks, drastically reducing manual configuration effort, while safeguarding the integrity and trustworthiness of the simulation results. It represents a significant step towards responsibly integrating advanced AI into high-stakes scientific and engineering computations, where solver reliability is paramount.