Prompt Decorators: A Declarative and Composable Syntax for Reasoning, Formatting, and Control in LLMs

• URL: http://arxiv.org/abs/2510.19850v1
• Date: Tue, 21 Oct 2025 17:35:49 GMT
• Title: Prompt Decorators: A Declarative and Composable Syntax for Reasoning, Formatting, and Control in LLMs
• Authors: Mostapha Kalami Heris,
• Abstract summary: This paper introduces Prompt Decorators, a declarative, composable syntax that governs behavior through compact control tokens.<n>Each decorator modifies a behavioral dimension, such as verbose reasoning style, structure, or tone, without changing task content.<n>It defines a unified syntax, scoping model, and deterministic processing pipeline enabling predictable and auditable behavior composition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are central to reasoning, writing, and decision-support workflows, yet users lack consistent control over how they reason and express outputs. Conventional prompt engineering relies on verbose natural-language instructions, limiting reproducibility, modularity, and interpretability. This paper introduces Prompt Decorators, a declarative, composable syntax that governs LLM behavior through compact control tokens such as +++Reasoning, +++Tone(style=formal), and +++Import(topic="Systems Thinking"). Each decorator modifies a behavioral dimension, such as reasoning style, structure, or tone, without changing task content. The framework formalizes twenty core decorators organized into two functional families (Cognitive & Generative and Expressive & Systemic), each further decomposed into subcategories that govern reasoning, interaction, expression, and session-control. It defines a unified syntax, scoping model, and deterministic processing pipeline enabling predictable and auditable behavior composition. By decoupling task intent from execution behavior, Prompt Decorators create a reusable and interpretable interface for prompt design. Illustrative use cases demonstrate improved reasoning transparency, reduced prompt complexity, and standardized model behavior across domains. The paper concludes with implications for interoperability, behavioral consistency, and the development of declarative interfaces for scalable AI systems.

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