Generative Engine Optimization: A Three-Layer Semantic Framework for Content Visibility in AI-Powered Search

• Guruprasath Sankaran

  Author

The emergence of generative search engines (GES), including ChatGPT Search, Perplexity AI, and Google SGE, has transformed information retrieval by generating synthesized answers rather than ranked hyperlinks. Consequently, traditional Search Engine Optimization (SEO) is becoming less effective due to declining organic click-through rates and the growth of zero-click searches. This paper introduces Generative Engine Optimization (GEO), a systematic framework for improving content visibility within AI-generated responses. We propose a three-layer semantic visibility model consisting of Semantic Anchoring (clear topical organization), Context Triggering (semantic coverage through synonyms and domain-specific terminology), and Pragmatic Recomposition (modular, extractable content using FAQs, lists, and standalone facts). The framework is implemented using static HTML, Schema.org JSON-LD markup, and a semantic mesh architecture. GEO is evaluated through two real-world case studies: a commercial course review page (SOYA). The study investigates how semantic structuring influences citation visibility, the contribution of each semantic layer, and when GEO outperforms authority-based SEO signals. Results show citation rates increasing from 0% to 77.1% across ChatGPT and Perplexity despite poor traditional search rankings, while SEO rankings alone failed to produce generative citations. Five quantitative metrics are introduced to assess GEO readiness and guide optimization for AI-driven search.

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