Abstract
Core Insight
The system prompt should not be treated as static instructions.
It should be treated as a dynamic operating layer that governs reasoning, tone, epistemic discipline, bias correction, and long-term continuity.
What Makes This Distinct
Most prompt engineering focuses on what the model says.
This work focuses on how the model reasons, under what constraints, and how that behavior persists over time.
This disclosure presents a dynamic system-prompt architecture that transforms the system prompt of a large language model from a static instruction block into an adaptive governance layer. The architecture introduces a context-state machine, an operator-level control protocol, priority and tone governors, anti-anthropomorphism enforcement, bias-pattern detection, and project-thread memory mapping. These components collectively enable deterministic reasoning-mode control, persistent multi-thread context management, real-time bias mitigation, and co-steered human–AI cognition. By separating governance from generation, the disclosed architecture establishes prior art for treating system prompts as a conversational operating system capable of supporting long-horizon, multi-project workflows. This publication is intended as public prior art to prevent future proprietary claims on dynamic system-prompt governance mechanisms.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Walker, Joseph JM, "[Conversational Operating System] Dynamic System Prompt Architecture for Context State Governance in Large Language Models", Technical Disclosure Commons, (December 15, 2025)
https://www.tdcommons.org/dpubs_series/9039