GEO, SEO, AEO, LLMO — what is the difference?
SEO optimizes the position in a results list. GEO optimizes presence in a generated answer — there is no “position 3” there, only this: the brand appears, is represented correctly and is cited. Or it isn't.
AEO (Answer Engine Optimization) and LLMO (Large Language Model Optimization) describe essentially the same field; GEO has established itself as the umbrella term. More important than the label: around 80 percent of the work overlaps with good SEO work. GEO does not replace SEO — it builds on it.
How AI answers come about
Generative systems answer from two sources: from training knowledge (what is said about a brand across the web — consistently and often repeated) and from real-time search (retrieval: what can currently be found, loaded and cited). Perplexity and AI Overviews are almost purely retrieval systems, ChatGPT mixes both.
You don't optimize “for the AI” but for the sources the AI reads from: your own website plus the third-party sources the systems trust — industry publications, directories, review platforms.
The GEO glossary
AI Overviews
AI-generated answer boxes above the classic Google results. They summarize several sources and link to them — whoever is cited there gains visibility before the list of links even begins. In depth: optimizing for AI Overviews →
AI Mode
Google's conversational search mode: users ask follow-up questions and receive answers instead of lists of links. What counts for websites here is appearing as a source of the answer.
AEO (Answer Engine Optimization)
An alternative label for the same field as GEO — focused on being served as a direct answer.
Answer-first structure
Content structure in which a question is first answered completely in 2–4 sentences before details follow. In practice one of the strongest levers for being cited. In depth: answer-first content →
Citation
A visible source attribution in an AI answer. Citations are the hard currency of GEO: they bring clicks and prove that the system trusts the source.
Entity
A uniquely identifiable “thing” — brand, person, product, place. The more consistent name, figures and facts are across all sources, the more confidently AI systems classify the entity. Contradictions lead to omission or misrepresentation. In depth: building entities →
GPTBot & AI crawlers
Crawlers of AI providers, such as GPTBot (OpenAI), Google-Extended or PerplexityBot. Anyone who blocks them via robots.txt or renders content only on the client side will not appear in AI answers — the most common silent GEO killer.
Grounding / RAG
Retrieval-augmented generation: before answering, the system searches for current sources and formulates the answer from them. Systems with grounding react much faster to improved content than pure training knowledge does.
llms.txt
A Markdown file in the website root that gives AI systems a curated overview of the most important content. A useful, inexpensive addition — not a substitute for accessible content. In depth: llms.txt explained →
LLMO (Large Language Model Optimization)
Another synonym label for GEO, focused on the training knowledge of language models rather than on real-time search.
Prompt set
A fixed set of 20–50 real customer questions that is queried regularly across several AI systems. The basis of every repeatable visibility measurement. In depth: the prompt set method →
Share of voice (AI)
The share of prompts in a prompt set in which your own brand appears in the answer — per system and over time. The central GEO metric for management reporting.
Frequently asked questions
Is GEO the same as AEO or LLMO?
At its core, yes: all three terms describe optimization for generative AI systems. GEO has established itself as the umbrella term; AEO emphasizes the answer format, LLMO the training knowledge of the models.
Do I need GEO in addition to SEO?
GEO is not a second program alongside SEO: around 80 percent of the work overlaps. An integrated strategy makes sense that adds answer formats, entity consistency, AI crawler access and monitoring.
How quickly does GEO work?
Systems with real-time search such as Perplexity or AI Overviews often react to improved content within weeks. By contrast, it takes months for facts to arrive in the training knowledge of the models.
How do I get started with GEO in practice?
First measure the baseline: define a prompt set and check whether and how the brand appears in AI answers. Then close the biggest gaps — usually answer-first content, fact consistency and technical accessibility. Or commission an AI visibility audit right away.