Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift
This work proposes a governance framework for non-developer practitioners to manage prompt design under model drift, which is an incremental improvement for prompt engineering.
This paper addresses the challenge of GPT-scale model drift in large language models by formalizing Natural Language Declarative Prompting (NLD-P) as a modular governance method. NLD-P separates provenance, constraint logic, task content, and post-generation evaluation directly in natural language, aiming to provide stable and interpretable control amidst evolving LLM behaviors.
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.