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

• URL: http://arxiv.org/abs/2602.22790v1
• Date: Thu, 26 Feb 2026 09:23:09 GMT
• Title: Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift
• Authors: Hyunwoo Kim, Hanau Yi, Jaehee Bae, Yumin Kim,
• Abstract summary: This paper reconceptualizes Natural Language Declarative Prompting as a declarative governance method.<n>We define minimal compliance criteria, analyze model-dependent schema receptivity, and position NLD-P as an accessible governance framework.
• Score: 4.138182440811707
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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