TY - CHAP A1 - Fischer, Alexander A1 - Burk, Louis A1 - Tavakoli Kolagari, Ramin A1 - Wienkop, Uwe T1 - Machine-Readable by Design: Language Specifications as the Key to Integrating LLMs into Industrial Tools T2 - Annals of Computer Science and Information Systems N2 - We propose a meta-language-based approach enabling Large Language Models (LLMs) to reliably generate structured, machine-readable artifacts referred to as Meta-Language-defined Structures (MLDS) adapted to domain requirements, without adhering strictly to standard formats like JSON or XML. By embedding explicit schema instructions within prompts, we evaluated the method across diverse use cases, including automated Virtual Reality environment generation and automotive security modeling. Our experiments demonstrate that the meta-language approach significantly improves LLM-generated structure compliance, with an 88 % validation rate across 132 test scenarios. Compared to traditional methods using LangChain and Pydantic, our MLDS method reduces setup complexity by approximately 80 %, despite a marginally higher error rate. Furthermore, the MLDS artifacts produced were easily editable, enabling rapid iterative refinement. This flexibility greatly alleviates the “blank page syndrome” by providing structured initial artifacts suitable for immediate use or further human enhancement, making our approach highly practical for rapid prototyping and integration into complex industrial workflows. Y1 - 2025 SN - 978-83-973291-6-4 U6 - https://doi.org/10.15439/2025F5613 SN - 2300-5963 VL - 43 SP - 531 EP - 542 PB - IEEE ER -