Beyond Next-Token Prediction: A Dialogical Educational Module for AI Developers on the Hierarchical Relational Organization of Large Language Models

Abstract

This publication contributes a unified conceptual framework to discussions of LLM development by interpreting training, inference, context engineering, grounding, evaluation, fine-tuning, agent design, and alignment as different aspects of hierarchical relational organization. Its central distinction is between information that is merely available to a model and information, frameworks, sources, or task constraints that actually govern continuation. This makes it possible to understand hallucination, sycophancy, framework substitution, retrieval failure, conceptual memory loss, and agent drift as related failures of constraint preservation rather than as isolated defects. The publication consequently shifts the developer’s attention from increasing generative capacity alone to constructing broader systems in which formal continuation remains answerable to evidence, task identity, conceptual framework, external tests, and human purposes.

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