Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift

The rapid evolution of large language models (LLMs) has transformed prompt engineering from a localized craft into a systems-level governance challenge. As models scale and update across generations, prompt behavior becomes sensitive to shifts in instruction-following policies, alignment regimes, and decoding strategies, a phenomenon we characterize as GPT-scale model drift. Under such conditions, surface-level formatting conventions and ad hoc refinement are insufficient to ensure stable, interpretable control. This paper reconceptualizes Natural Language Declarative Prompting (NLD-P) as a declarative governance method rather than a rigid field template. NLD-P is formalized as a modular control abstraction that separates provenance, constraint logic, task content, and post-generation evaluation, encoded directly in natural language without reliance on external orchestration code. We define minimal compliance criteria, analyze model-dependent schema receptivity, and position NLD-P as an accessible governance framework for non-developer practitioners operating within evolving LLM ecosystems. Portions of drafting and editorial refinement employed a schema-bound LLM assistant configured under NLD-P. All conceptual framing, methodological claims, and final revisions were directed, reviewed, and approved by the human author under a documented human-in-the-loop protocol. The paper concludes by outlining implications for declarative control under ongoing model evolution and identifying directions for future empirical validation.

Machine-Facing English: Defining a Hybrid Register Shaped by Human-AI Discourse

Machine-Facing English (MFE) is an emergent register shaped by the adaptation of everyday language to the expanding presence of AI interlocutors. Drawing on register theory (Halliday 1985, 2006), enregisterment (Agha 2003), audience design (Bell 1984), and interactional pragmatics (Giles & Ogay 2007), this study traces how sustained human-AI interaction normalizes syntactic rigidity, pragmatic simplification, and hyper-explicit phrasing - features that enhance machine parseability at the expense of natural fluency. Our analysis is grounded in qualitative observations from bilingual (Korean/English) voice- and text-based product testing sessions, with reflexive drafting conducted using Natural Language Declarative Prompting (NLD-P) under human curation. Thematic analysis identifies five recurrent traits - redundant clarity, directive syntax, controlled vocabulary, flattened prosody, and single-intent structuring - that improve execution accuracy but compress expressive range. MFE's evolution highlights a persistent tension between communicative efficiency and linguistic richness, raising design challenges for conversational interfaces and pedagogical considerations for multilingual users. We conclude by underscoring the need for comprehensive methodological exposition and future empirical validation.