AEO, Answer Engine Optimization, is the work of getting cited when an AI service answers a question. Classic SEO chases the ranking on Google’s results page. AEO chases the mention inside the answer itself, in ChatGPT, Perplexity or Google AI Overviews.
Data verified: June 2026
The discipline of getting cited when an answer engine responds to a question. Instead of ranking a link, you work to have ChatGPT, Perplexity and Google AI Overviews name your brand in the answer. The difference from SEO is simple: SEO wants the click, AEO wants the mention.
The work of shaping what generative models actually write in their answers. The term comes from a 2023 research paper and overlaps closely with AEO. Where AEO is used broadly for all answer engines, GEO points at the generative ones in particular: Google AI Overviews and Perplexity.
The narrowest of the three. Here the focus is the language model itself, meaning how ChatGPT or Claude perceives, describes and ranks your brand. Think reputation management, aimed at the model rather than at an audience.
Google’s AI-generated summaries at the top of the results page. They pull from several sources and cite them with links. Becoming one of those sources is the whole point, because that is where attention lands before anyone scrolls.
A search answered right in the results, without the user clicking through. The share has grown alongside answer boxes and AI summaries. The consequence is uncomfortably concrete: visibility can no longer be measured in clicks alone.
A service that hands you a finished answer instead of a list of links. Perplexity is the cleanest example. Google is moving the same way with AI Overviews, and the line between search engine and answer engine keeps blurring.
When an AI service names the source behind an answer, as a link or a name. Citations are the currency of AEO. Without them you get read but never credited, and the attention goes to someone else.
The technique where a model splits one question into several sub-questions, searches each, and stitches the answers together. Google has described the pattern for its AI Mode. It means your content can get pulled in for questions you never wrote for directly.
A method where the model retrieves external documents before answering, rather than relying on what it remembers from training. Most answer engines use a variant. That is why it matters that your content is easy to retrieve and quick to parse.
A distinct thing a search engine recognizes: a company, a person, a product, a place. Models reason in entities, not loose keywords. The sharper your entity is defined, the easier you are to surface.
Google’s database of entities and the relationships between them. It sits behind the knowledge panel on the right of the results. A place in the graph gives models a fixed anchor when they describe you.
Anchoring an AI answer in an external, verifiable source rather than the model’s free guess. Good grounding cuts down errors. It is also where the opportunity sits: becoming the source the answer rests on.
When a model states something that sounds right but is not. In a brand context that can mean a wrong price, the wrong founder, or a service you do not offer. Clear, consistent facts on your own pages are the best antidote.
A short Markdown file at the root of your site that points AI models to the content worth reading and where it lives. The proposal is young and not yet an official standard. Google Search does not use it and does not require it for AI visibility. Chrome Lighthouse, on the other hand, runs an experimental agentic-browsing audit that flags a missing file, because a tidy summary helps AI agents and browser extensions understand your structure faster. Think robots.txt, written for language models.
When an AI agent navigates the web on its own: clicking, reading and pulling information on a user’s behalf. The agent does not see the page the way a person does. It leans on structure, semantic HTML and the accessibility tree. Chrome Lighthouse has started auditing sites for exactly this, so machine readability becomes its own quality metric.
The browser’s structured map of the page, derived from the DOM, where every element has a role, a name and a state. Screen readers have always used it. Now AI agents read the same tree to work out what a button does or what a form field is called. Semantic HTML, real buttons and labelled fields make you legible to both assistive tech and agents.