AEO (Answer Engine Optimization), GEO (Generative Engine Optimization) and LLMO (Large Language Model Optimization) look at the same question from three angles: when someone gets an answer instead of a list of links, is your brand inside it? AEO aims to be selected as the single direct answer, GEO aims to appear in answers that AI search engines synthesize from several sources, and LLMO aims to shape what language models "know" about your brand. For most brands the right move is not to pick one, but to run all three as a single program on top of solid SEO, and to measure the outcome.
Key takeaways
- AEO grew up in the era of featured snippets, voice assistants and answer boxes; its goal is to be the answer.
- GEO got an academic framework in 2023 through the paper by Aggarwal et al.; it measures and improves visibility inside generated answers.
- LLMO is the least standardized term; it covers how a model represents your brand from its training data and how consistent your entity information is.
- All three rely on crawlable, trustworthy, evidence-based content; Google states there are no additional requirements to appear in AI Overviews.
- Let data, not acronyms, set your priorities: first measure where you stand on the questions your audience actually asks.
What is AEO? Optimizing for answer engines
AEO took off when search engines and assistants began giving a direct answer instead of ten blue links. Featured snippets, "People also ask" boxes and voice-assistant replies are its classic targets.
The core techniques are concrete:
- Question-style headings followed immediately by a short answer paragraph that makes sense on its own.
- Easily quotable structures such as definitions, step lists and tables.
- Structured data (schema.org) where it genuinely applies.
There is an important nuance. In 2023 Google restricted FAQ rich results largely to well-known, authoritative government and health websites and deprecated HowTo rich results. Old AEO recipes such as "add FAQ schema and win the answer box" no longer work as they once did. AEO's lasting value lies in the discipline of writing answer-first, not in markup tricks. Success is binary: you are the selected answer or you are not, often without a click.
What is GEO? Visibility in generative search engines
The term GEO gained an academic footing with "GEO: Generative Engine Optimization" by researchers from Princeton and partner institutions (Aggarwal et al., arXiv:2311.09735, KDD 2024). It defines systems like Perplexity as "generative engines": they retrieve sources, then a language model synthesizes them into one cited response.
The authors proposed visibility metrics that capture how much of a response a source accounts for, plus a benchmark of queries called GEO-bench. In their experiments, methods such as adding citations, adding quotations and including statistics boosted visibility by up to 40%, while keyword stuffing did not help noticeably. They also reported that effectiveness varies by domain. These are controlled experiments; live products may behave differently.
What separates GEO from AEO is that there is no single "winning answer". A response can mention several brands, cite some sources and ignore others. Success in GEO is therefore proportional rather than binary: in how many answers you appear, in what position you are mentioned, and how often you are cited as a source. For a broader introduction, see our guide on what is GEO.
What is LLMO? Making language models "know" your brand
LLMO is the least settled term, used differently by agencies and vendors. The most defensible definition: efforts to make what a language model says about your brand accurate, current and favorably framed.
Unlike GEO, it also targets the model's "parametric" knowledge learned in training, not just live retrieval. When a user asks ChatGPT "what are the best brands in category X?" without web search, the answer largely depends on how often, and in what context, your brand appeared in the model's training data.
LLMO levers are longer term:
- Describing the brand name, category and key attributes consistently across the web.
- Being mentioned in independent publications, industry lists, comparison content and user reviews.
- A deliberate access policy for training and search crawlers: OpenAI documents GPTBot for model training and a separate OAI-SearchBot for visibility in ChatGPT search.
Another frequently cited tool, llms.txt, is a proposal published by Jeremy Howard in 2024. It has not been officially confirmed that major AI providers use it as a ranking signal; see what is llms.txt for details.
AEO vs GEO vs LLMO: comparison table
| Dimension | AEO | GEO | LLMO |
|---|---|---|---|
| Target system | Featured snippets, voice assistants | AI search that retrieves and synthesizes sources | The model's own knowledge and brand representation |
| Unit of success | Being the one selected answer | Rate of mentions and citations in answers | Accurate, consistent brand description |
| Main lever | Q&A structure, short clear answers | Evidence, statistics, quotable sources | Third-party mentions, entity consistency |
| Time horizon | Weeks | Weeks to months | Months to model releases |
| Measurement | Snippet ownership | Presence rate, citations, competitor share | Analysis of brand descriptions across repeated prompts |
In practice the boundaries blur: one good comparison page can serve all three goals.
Common ground: why all three rest on SEO
All three approaches share a precondition: your content must be crawlable, indexable and trustworthy. Google's "AI features and your website" documentation states plainly that there are no additional requirements or special optimizations needed to appear in AI Overviews and AI Mode, and that foundational SEO best practices still apply. Perplexity and ChatGPT search also work by retrieving web sources, so a site that blocks their crawlers in robots.txt largely gives up its chance of being cited in those answers.
So the "SEO is dead" narrative misleads; see GEO vs SEO and the glossary entry on AEO vs GEO.
Which should you focus on? A step-by-step priority order
Rather than debating labels, follow this sequence:
- Measure your baseline. Identify 30 to 100 questions your audience actually asks and put them to several assistants in your chosen market and language. Calculate the share of valid answers that mention your brand, your presence rate. A tool such as GeoDog does this while storing every real answer and its sources.
- Fix technical access. Make sure you are not accidentally blocking search crawlers such as Googlebot, OAI-SearchBot and PerplexityBot.
- Apply AEO discipline. On key pages, put the question in the heading and the short answer right below it.
- Add evidence for GEO. Include original data, sourced statistics and expert input. Our article on how to write content AI quotes goes deeper.
- Strengthen external signals for LLMO. Work on independent reviews, industry lists and consistent company information.
- Re-measure with the same sample. Track change with the same question set at regular intervals.
As an example, a hypothetical accounting software company in Manchester might find that it appears in only one in five answers to "best accounting software for small businesses". If the cited sources are mostly comparison blogs, the priority lies with GEO and LLMO; if its own site is cited but the brand name is missing from the answer, the problem is more likely that its content does not position the brand clearly. For measurement methods, read how to measure AI visibility and our methodology page.
Limitations and honest expectations
AI answers are not deterministic: the same question can produce different answers at different times, in different locations and with different model versions. A single screenshot is not evidence; meaningful results require a large enough sample and an honest calculation that removes failed answers from the denominator. No method, agency or tool can guarantee a place in an answer. The realistic goal is to raise the probability and track it measurably.
Terminology is another trap: an "AEO package", a "GEO audit" and an "LLMO service" are often similar in substance. Look past the label and ask: which questions, on which assistants, in which market and language are measured? How is the denominator calculated? Are competitors compared on the same sample? If a report cannot answer these, the acronym does not matter.
FAQ
Are AEO, GEO and LLMO the same thing?
No, but they overlap heavily. AEO focuses on a single direct answer, GEO on visibility in synthesized answers, and LLMO on how the model represents your brand. In practice the same content work serves all three.
Where should a small business start?
With measurement. After that, technical access and answer-first content (AEO) deliver the fastest gains. GEO and LLMO work pays off over a longer horizon.
Can you get into a model's training data on purpose?
There is no direct route. What you can do is make sure your brand is mentioned consistently in credible, publicly accessible sources; how that will be reflected in future models remains uncertain.