Modular Meta-Languages for Structured Instructions: A Novel Approach for LLM Integration into Evolving Engineering Toolchains

Abstract

Large Language Models are increasingly used to generate structured engineering artifacts, yet the instruction artifacts that govern this generation are rarely treated as modeling artifacts in their own right. They typically appear as monolithic prompt blocks, schemas, or informal examples. When tools, metamodels, APIs, or domain vocabularies evolve, stable domain concepts, volatile tool details, validators, and examples drift together in a single prompt contract. This paper puts forward the thesis that prompt-level instruction artifacts should be understood as versioned modeling languages: explicit artifacts that define the LLM-facing structure required to obtain tool-consumable outputs. We realize this idea through Modular Meta-Language-defined Structure Instructions (MMLDSI), a modular architecture that decomposes structured instructions into versioned modules with explicit interfaces, profiles, adapters, validators, examples, and deterministic rule-deck resolution. Changes to tools or domain concepts can then be represented at the affected modules and profiles rather than hidden inside whole-prompt rewrites. The approach is demonstrated in VR scene generation and automotive security modeling. Across a balanced 320-artifact VR subset, required-key validity reaches 88.1%, while a stricter prompt-contract audit accepts 80.9% and exposes remaining tool-readiness gaps. An offline monolith-to-modular pilot maps 12 legacy feature families and resolves 343 case-specific decks without dependency gaps. Our contribution is not another prompting technique, but a model-management perspective on instruction artifacts for evolving LLM toolchains.