Abstract

Most people are using AI wrong. Not because they lack intelligence or technical skill, but because they are treating a dynamic reasoning system like a static lookup tool. Ask, receive, move on. What that pattern leaves behind is the most capable part of the system. This guide introduces the Human Recursive Interaction System (HRIS), a practical field- tested framework for maximizing the effectiveness of LLM interaction through continuous constraint management over time. HRIS reframes human–LLM engagement as an ongoing process in which the human participant actively maintains structure, scope, and epistemic discipline. Hallucination and drift are treated not as anomalies but as predictable failure modes of unconstrained probabilistic systems. HRIS introduces epistemic closure as a concrete, achievable interaction goal: a state where every accepted claim is either supported, qualified, or explicitly flagged as unknown. The framework provides a repeatable protocol that lets any user reconstruct disciplined reasoning conditions from scratch, every session, regardless of model memory limitations. By shifting the locus of control from the model to the operator, HRIS demonstrates that the quality of AI-assisted reasoning is not solely a function of the model, but of the structure imposed by the human operator.