Generative Engine Optimization: A Technical Framework for AI Search Visibility
Search technology is undergoing a fundamental transformation driven by the rise of generative AI systems (LLM-based search engines). While traditional search engines direct users to the most relevant web page, next-generation systems synthesize information directly into a single answer. This shift has evolved the goal of digital visibility from "ranking highly" to "being selected as a trusted source by AI." This article examines the discipline of Generative Engine Optimization (GEO) across information retrieval, natural language processing, RAG architecture, vector representations, and machine readability; by synthesizing current research, it proposes a five-layer visibility model called the AI Visibility Framework (AIVI). The study argues that GEO is not merely an optimization technique but a strategic paradigm redefining how information is produced, distributed, and validated in the age of AI.
1. Introduction: From Search Engines to Answer Engines
For the first two decades of the internet, search engines took on the task of...
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