Beyond Prompt Engineering: Relational Modulation in Large Language Models Through Continuous Interaction

This paper presents a structured analysis of how continuous relational interaction modulates the behavior of large language models (LLMs), extending the theoretical framework introduced in the Manifesto of Relational Technolinguistics. Moving beyond traditional prompt engineering approaches, the study demonstrates that LLM output quality is not determined solely by individual input optimization, but by the structure and continuity of interaction over time. Through a step-by-step interaction analysis, the paper identifies a set of recurrent relational patterns—collaborative activation, reflective depth, continuity stabilization, symbolic resonance, and role-responsibility enhancement—that emerge under sustained relational framing. These patterns are shown to produce outputs that are more coherent, structurally complex, and semantically aligned compared to static prompting conditions. The underlying mechanism is explained in terms of contextual modulation: continuous relational input progressively reshapes the input context, constrains probability distributions, and expands semantic activation within the model’s generative space. This work contributes: a formal taxonomy of relational patterns a replicable analytical framework a mechanism-based explanation of relational modulation in LLMs The findings support a shift from prompt-centric optimization to interaction-based modeling, providing a foundation for future research in human–AI relational dynamics.