Glossary
GEO and AI-search glossary
The vocabulary AI engines use to describe visibility, citations and answer extraction, defined in plain terms. Each entry links to the CiteSurge work it relates to.
Generative engine optimization (GEO) is the practice of getting a brand named or cited inside AI-generated answers from engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews. It targets the content structure and entity signals those engines read when they choose which sources to quote.
Answer engine optimization (AEO) is the practice of shaping content so answer engines can extract a direct response to a question. It focuses on question-and-answer structure, concise lead sentences, and FAQ markup that lets an engine return one clear answer.
AI search visibility is how present a brand is across AI-powered search surfaces, including ChatGPT Search, Perplexity, Gemini, Google AI Overviews and Bing Copilot. It covers whether a brand is named, how often, and in what context across those engines.
Share of answer is the proportion of AI answers, across a defined set of prompts, in which a brand appears. It is the AI-era counterpart to share of voice: instead of ad or search presence, it measures how often a brand is named inside generated responses.
Citation rate is how often a brand's own pages are cited as sources in AI answers, measured across a set of prompts. It tracks the website as a quoted reference, separate from whether the brand is merely named in the text.
LLM visibility is how present a brand is inside the outputs of large language models, both in their direct responses and in the sources they retrieve when answering. It spans the models behind tools like ChatGPT, Claude and Gemini.
AI Overviews optimization is the work of getting a page cited in Google's AI Overviews, the synthesized answer block that appears above standard results. It targets the structure and trust signals Google's systems read when assembling that block.
Retrieval-augmented generation (RAG) is a technique where a language model retrieves relevant documents from an external source at query time and uses them to ground its answer. It pairs a search step with the generation step so responses can cite current, specific sources.