Generative engine optimization
Digital marketing technique
From Wikipedia, the free encyclopedia
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.[1] The practice influences the way large language models (LLMs), such as ChatGPT, Google Gemini, Claude, and Perplexity AI, retrieve, summarize, and present information in response to user queries.[2] Related terms include answer engine optimization (AEO)[2] and artificial intelligence optimization (AIO).[3]
The concept of GEO first appeared in response to generative AI technologies being integrated into mainstream search and information retrieval systems.[4]
Tools are used to monitor how websites and brands are cited, referenced, or incorporated into responses produced by large language models.[5]
Practitioners also measure how often a brand is mentioned in AI-generated answers, which URLs or domains are cited in those answers, and a brand’s share of voice relative to competitors.[1]
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.[2]
Answer engine optimization (AEO) is sometimes used specifically in reference to systems designed to return direct answers rather than lists of links, such as voice assistants and featured snippet formats, predating the widespread deployment of large language model-based search.[6] 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.[citation needed] Artificial intelligence optimization (AIO) is used in academic and practitioner contexts as a broader umbrella term covering any practice aimed at structuring content and messaging so that it can be effectively interpreted by AI systems acting as an audience or intermediary.[7] 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.[2]
Practitioner tactics
Practitioners working on generative engine optimization focus on a few recurring approaches, drawn from trade and practitioner publications.
Entity disambiguation
Consistent naming, location data, category descriptors, and structured data markup across web properties help generative models identify and distinguish an entity accurately. The approach draws on knowledge graph optimisation principles, where how coherently an entity is described across linked data sources affects how prominently it appears in model outputs.[citation needed][citation needed]
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 the relevance of a source to the query, the position of its citations within a response, and the extent of content attributed to it.[1]