Large language models (LLMs) have undergone a rapid and widespread diffusion, profoundly impacting both the world of work and education. The need to make the most of these tools has led to the birth of a new discipline: prompt engineering. It enables the development and optimization of prompts, the initial inputs provided to a language model to generate responses, maximizing LLM capabilities while also revealing their limitations and areas for potential improvement. One area where development remains limited is database design, despite it is the process of structuring data for efficient management, a crucial step that demands respect of rules and constraints.