Reframing SEO for the Age of Generative Engines

Abstract

The paper explores how results from traditional search engines, such as Google and Bing, differ from those generated by large language model (LLM) – based tools, including ChatGPT, Gemini, and Perplexity. It examines whether the established principles of Search Engine Optimisation (SEO) still influence outcomes in generative systems, a process referred to here as Generative Engine Optimisation (GEO). Using identical keyword and natural language queries, the study compares the consistency and character of retrieved information across platforms. Findings indicate only partial overlap between traditional and generative systems. Search engines prioritise factual accuracy and relevance, while LLMs tend to provide broader, interpretative responses shaped by user intent. These differences suggest that generative AI is reshaping search behaviour and the ways people access and evaluate information online. The paper highlights implications for marketing communication and the adaptation of SEO strategies in AI-driven environments.

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