TY - CHAP A1 - Buchmann, Thomas A1 - Peinl, René A1 - Schwägerl, Felix T1 - White-box LLM-supported Low-code Engineering: A Vision and First Insights T2 - Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems N2 - Low-code development (LCD) platforms promise to empower citizen developers to define core domain models and rules for business applications. However, as domain rules grow complex, LCD platforms may fail to do so effectively. Generative AI, driven by large language models (LLMs), offers source code generation from natural language but suffers from its non-deterministic black-box nature and limited explainability. Therefore, rather than having LLMs generate entire applications from single prompts, we advocate for a white-box approach allowing citizen developers to specify domain models semi-formally, attaching constraints and operations as natural language annotations. These annotations are fed incrementally into an LLM contextualized with the generated application stub. This results in deterministic and better explainable generation of static application components, while offering citizen developers an appropriate level of abstraction. We report on a case study in manufacturing execution systems, where the implementation of the approach provides first insights. KW - Model-driven engineering KW - large language models KW - low-code KW - semiformal KW - artificial intelligence Y1 - 2024 U6 - https://doi.org/10.1145/3652620.368780 SP - 556 EP - 560 PB - ACM CY - New York, NY, USA ER -