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

TL;DR

Prompt Decorators provide a declarative, composable syntax for controlling LLM behavior, enhancing transparency, modularity, and reproducibility in prompt design.

Contribution

This work introduces a formal syntax and framework for controlling LLM outputs through compact, declarative decorators, improving prompt modularity and interpretability.

Findings

Enhanced reasoning transparency and control

Reduced prompt complexity and improved standardization

Demonstrated applicability across diverse domains

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.