AIRT::The Master Leveling Prompt: A Front-End User Methodology for Human-AI Cognitive Coherence Across Platforms

This paper introduces and documents the Master Leveling Prompt (MLP) — a methodology developed by an independent front-end AI user and researcher for achieving rapid cognitive coherence between a human practitioner and multiple AI systems across different platforms. The MLP is neither a jailbreak nor a system prompt in the conventional engineering sense; it is a structured epistemological bridge that encodes the researcher’s identity, philosophical framework, terminological ecosystem, and operational norms into a portable, living document. Developed through 1.5+ years of daily cross-platform AI red teaming practice — spanning Claude, Gemini, ChatGPT, DeepSeek, and Copilot — the MLP addresses a gap that existing AI interaction literature has largely overlooked: the front-end user’s perspective on building sustainable, high-coherence human-AI working relationships without access to back-end engineering tools. This paper documents the MLP’s conceptual foundations, structural architecture, iterative development process (v1.0 through v1.9), multi-AI validation methodology, and implications for AI alignment discourse from a humanistic standpoint. The MLP is positioned as a contribution to what the author terms front-end user research — a distinct positionality within AI research that complements, but does not replicate, back-end engineering approaches.