Dictionary and technical nomenclature of Generative Engine Optimization (GEO)

The transition of the digital ecosystem toward conversational interfaces driven by Large Language Models (LLMs) has caused the collapse of the deterministic Search Engine Optimization (SEO) paradigm (Aggarwal et al., 2023), opening the way for a new dynamic of web positioning that replaces old keywords with vectors and vector density. It is acknowledged that, within the current attention economy, marked by "infobesity" and pervasive structural noise, corporate entities face an unprecedented challenge: being algorithmically invisible to Artificial Intelligence systems (Blanco, 2026). As web traffic shifts towards a Zero-Click environment, the imperative to organize knowledge within a machine-readable and thermally efficient architecture has become increasingly critical. However, the commercial adoption of Generative Engine Optimization (GEO), along with the disruption of synthetic content lacking a scientific foundation, has led to a crisis of semantic, technical, and structural ambiguity, saturating GEO-related vectors with imprecise information, commonplaces, and inaccurate data. For this reason, the present document aims to establish a standardized ontology and a technical dictionary that unifies the fundamentals of machine learning, thermodynamic efficiency, and minimalist interface design under the Kūkan-Ha framework, providing a common and mathematically rigorous language. Keywords: Context Window, Generative Engine Optimization, AI Engine Optimization www.ibresearchlab.com