Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift
arXiv:2602.22790v1 Announce Type: new 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-depe
arXiv:2602.22790v1 Announce Type: new 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.
Executive Summary
This article introduces Natural Language Declarative Prompting (NLD-P), a modular governance method for designing prompts in large language models (LLMs) to mitigate model drift. The authors reconceptualize NLD-P as a declarative control abstraction that separates provenance, constraint logic, task content, and post-generation evaluation. They formalize NLD-P as a modular control abstraction and analyze model-dependent schema receptivity. The authors argue that NLD-P is an accessible governance framework for non-developer practitioners operating in evolving LLM ecosystems.
Key Points
- ▸ NLD-P is a modular governance method for designing prompts in LLMs to mitigate model drift.
- ▸ NLD-P reconceptualizes prompt design as a declarative control abstraction.
- ▸ NLD-P separates provenance, constraint logic, task content, and post-generation evaluation.
Merits
Strength in Addressing Model Drift
NLD-P provides a systematic approach to addressing model drift, which is a significant challenge in LLM development. By separating provenance, constraint logic, task content, and post-generation evaluation, NLD-P offers a modular control abstraction that can adapt to evolving LLM ecosystems.
Accessible Governance Framework
NLD-P is designed to be an accessible governance framework for non-developer practitioners, making it a valuable contribution to the field of LLM development.
Demerits
Limited Empirical Validation
The article concludes with a call for future empirical validation, which is a limitation of the current study. Further research is needed to confirm the effectiveness of NLD-P in real-world applications.
Expert Commentary
The article makes a significant contribution to the field of LLM development by introducing NLD-P, a modular governance method for designing prompts. The authors provide a clear and concise explanation of NLD-P and its key components, making it accessible to a wide range of readers. However, the article would have benefited from more extensive empirical validation to confirm the effectiveness of NLD-P in real-world applications. The development of NLD-P also raises important policy questions about the governance of LLMs and the need for more systematic approaches to addressing model drift.
Recommendations
- ✓ Future research should focus on empirical validation of NLD-P in real-world applications.
- ✓ The development of NLD-P should be integrated with existing governance frameworks for LLMs to ensure a more systematic approach to addressing model drift.
