🤖 AI Summary
The continuous evolution of large language models induces prompt behavior drift, undermining the stability and controllability of traditional prompt engineering. To address this, this work proposes the Natural Language Declarative Prompting (NLD-P) framework, which reconceptualizes prompt design as a declarative governance approach. NLD-P modularly abstracts source specifications, constraint logic, task content, and post-generation evaluation, encoding control structures entirely in natural language without external code orchestration. This framework elevates prompt engineering to a system-level governance paradigm, enabling non-technical users to achieve interpretable and stable prompt management amid model evolution. The study defines minimal compliance criteria for NLD-P, validates its applicability across model versions, and introduces a human-in-the-loop verification mechanism alongside model-dependent pattern acceptability analysis, offering a novel pathway for prompt control in dynamic model environments.
📝 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.