Emergent Prompt Engineering (EPE): A Structural Framework for Behavioral Architecture in Large Language Models A Behavioral Approach to Conversational Trajectory Regularities

Current prompt engineering techniques primarily operate via direct instruction or formatting constraints, optimizing short-term outputs without shaping long-term generation dynamics. This document formalizes the framework of Emergent Prompt Engineering (EPE). Conceptualized as a structural framework, EPE explores the induction of stable behavioral regimes via recursive semantic constraints. This approach is grounded in a longitudinal exploration across 160 conversational turns (Grok 4.20) and 100 conversational turns (Claude-Haiku 4.5), supplemented by a multi-model observation protocol (Gemini, Claude, GPT-4o). This paper introduces the theoretical foundations of EPE and its core case study, the Prompt Coherence Engine (PCE). We introduce here a proposal for a falsifiable protocol designed to formally test the framework’s core hypotheses, transitioning from exploratory observations toward a standardized empirical validation framework. Academic Positioning Note: The protocol presented within this document does not constitute a validation in itself of EPE or PCE, but a proposal for a falsifiable protocol allowing a rigorous test of their hypotheses.