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Generative engine optimization

Generative engine optimization (GEO) is one of the names given to the practice of structuring digital content and managing online presence to improve visibility in responses generated by generative artificial intelligence (AI) systems. The practice influences the way large language models (LLMs), such as ChatGPT, Google Gemini, Claude, Perplexity AI and Copilot retrieve, summarize, and present information in response to user queries. Related terms include answer engine optimization (AEO) and artificial intelligence optimization (AIO).

Terminology
Several overlapping terms describe related practices, and usage varies across practitioners, vendors, and publications. No consensus definition distinguishing these terms had been established in the academic literature as of early 2026, and the terms are frequently used interchangeably in trade and practitioner contexts. Large language model optimization (LLMO) is used in some practitioner contexts with a narrower focus on influencing a model's parametric knowledge rather than on retrieval-based systems.Large language model optimization (LLMO) is used in some practitioner contexts with a narrower focus on influencing a model's parametric knowledge rather than on retrieval-based systems. AI SEO is used when the practice is positioned as a direct continuation of traditional search engine optimization workflows adapted for AI-mediated discovery environments. == Mechanisms ==
Mechanisms
Visibility in generative AI responses works differently from traditional search engine ranking. A few key mechanisms explain why. Retrieval-augmented generation Many deployed AI systems supplement parametric model knowledge with retrieval-augmented generation (RAG), in which a query is used to retrieve relevant document segments from an external index, and those segments are incorporated into the model's context window before a response is generated. When descriptions conflict across sources, though, the result is often a hedged or absent mention in AI-generated responses. == Practitioner tactics ==
Practitioner tactics
Practitioners working on generative engine optimization focus on a few recurring approaches, drawn from trade and practitioner publications. == Factors influencing generative engine optimization ==
Factors influencing generative engine optimization
Generative engine optimization is influenced by how content is incorporated into responses generated by large language models. In generative engines, visibility depends on factors such as a source's relevance to the query, the position of its citations within a response, and the extent of content attributed to it. == See also ==
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