GEO, AEO & LLMO: a glossary of AI visibility
Whether GEO, AEO, LLMO or AI Overviews — a whole vocabulary has emerged around visibility in AI search systems in a very short time. This glossary explains the key terms compactly and precisely, so you can take part in the conversation with confidence.
Core terms
GEO (Generative Engine Optimization)
Generative Engine Optimization (GEO) is the deliberate optimization of content so that generative AI systems such as ChatGPT, Perplexity or Google AI Overviews cite and recommend it in their answers. Unlike classic SEO, GEO does not aim for a spot in the list of results but for inclusion in the answer the AI formulates itself.
AEO (Answer Engine Optimization)
Answer Engine Optimization (AEO) optimizes content so that answer engines — voice assistants, featured snippets or AI chatbots — return it directly as the answer to a specific user question. AEO overlaps with GEO but focuses more on precise, extractable answers than on longer generative recommendations.
LLMO (Large Language Model Optimization)
Large Language Model Optimization (LLMO) covers the measures companies use to deliberately influence how large language models such as GPT, Claude or Gemini represent them in training data, grounding sources and generated answers. The term is often used synonymously with GEO but explicitly emphasizes the model level rather than the search surface.
AI Overviews
AI Overviews are AI-generated summaries from Google that appear above the classic organic search results and answer questions directly, often with source references. They change search behavior: many users find their answer in the overview without visiting a linked page.
More terms around AI visibility
AI Visibility
AI visibility describes how often, how prominently and how accurately a company is named, recommended or cited in the answers of generative AI systems. It is the AI counterpart to classic search-engine visibility, but it cannot be measured through rankings — only through mentions and citations in AI answers.
Grounding
Grounding is the process by which an AI system bases its answers on verifiable, external sources instead of generating them solely from internal model knowledge. Well-grounded answers reference concrete web pages or documents and reduce the risk of a hallucination.
Citation
A citation is an AI system’s reference to an external source within a generated answer — for example as a link, footnote or source attribution. For companies, the citation is the central success metric in AI search — comparable to a ranking position in classic search.
Hallucination
A hallucination is a statement produced by an AI system that is factually wrong yet reads confidently and plausibly. Hallucinations arise, among other reasons, when the model has no reliable grounding sources for a topic.
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is an architecture in which a language model retrieves relevant documents from an external knowledge source before generating an answer and includes them as context in the prompt. RAG improves the timeliness and factual accuracy of AI answers, because the model no longer relies on its trained knowledge alone.
Prompt
A prompt is the input — usually a text or a question — that a user passes to an AI system to trigger an answer or an action. The wording and context of the prompt significantly influence which sources the model draws on and how the answer turns out.
AI Crawler
AI crawlers are automated bots that providers such as OpenAI (GPTBot), Anthropic (ClaudeBot), Perplexity (PerplexityBot) or Google (Google-Extended) use to capture web content for training or live answers. For a page to appear in AI answers, these crawlers must be technically allowed to access it — controlled, for example, via the robots.txt.
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