AEO & GEO Glossary

The disciplines#

AEO — Answer Engine Optimization#

Making a page readable, parseable and quotable by systems that answer a question directly instead of returning links. The success condition is being the source a generated answer is built from. See the pillar page for the four preconditions.

GEO — Generative Engine Optimization#

The same work, framed around how you are represented inside generated text rather than whether you are cited. In practice the techniques are identical; the distinction is emphasis. Compared properly in GEO vs SEO vs AEO.

LLMO — Large Language Model Optimization#

A third label for the same territory, framed model-first. Useful mainly as a search term.

SEO — Search Engine Optimization#

Optimising for a ranking that produces a click. Shares its entire technical foundation with AEO — crawlability, speed, structure — and diverges on what counts as success.

Machine-readable surfaces#

llms.txt#

A Markdown file at /llms.txt that gives an AI crawler a curated map of a site: what it is, which pages matter, and where the machine-readable version of each one lives. It is a proposed convention, not a standard, and it is cheap enough that the argument about adoption is beside the point. Implementation walkthrough: How I added llms.txt in 15 minutes.

Markdown mirror (.md variant)#

A plain-Markdown twin of an HTML page, served at the same URL with .md appended — this page has one. No navigation, no scripts, no cookie banner: just the content. AI crawlers parse it far more reliably than a rendered React page, and it costs nothing to serve both. The HTML stays canonical for search; the mirror is noindex so Google does not see a duplicate.

AGENTS.md#

A file describing a repository or site to coding agents — conventions, commands, constraints. Adjacent to llms.txt but aimed at agents that will write code against you, not summarise you.

robots.txt#

The fetch-time control. Tells crawlers which paths they may request. It is a request honoured by convention, not a fence — see RFC 9309. Distinct from noindex, which governs use rather than fetching: a page blocked in robots.txt is never fetched, so its noindex is never seen.

noindex#

A directive, in a <meta name="robots"> tag or an X-Robots-Tag header, excluding a page from an index. For AI crawlers it reads as a full exclusion from answers and training. The header form is invisible in the HTML, which makes it the harder half of the 3-word tag.

Structure and meaning#

JSON-LD#

Structured data as a JSON block in <script type="application/ld+json">, decoupled from the markup — which is why it survives a redesign and Microdata does not. The vehicle for saying who wrote a page, when, and what type of thing it is. See Help AI understand your content with JSON-LD.

Schema.org#

The shared vocabulary JSON-LD is written in: Article, FAQPage, Person, SoftwareApplication, BreadcrumbList. A type is a claim about what the page is; the properties are claims about its content.

FAQPage#

Schema.org markup declaring question-and-answer pairs on a page. The most common way it goes wrong is being typed twice — once as visible prose, once in the schema — after which the copies drift and the markup becomes misleading. Derive it from the visible text instead.

dateModified#

When the content last meaningfully changed, as distinct from datePublished, which never changes. Models weigh it heavily for anything time-sensitive. Bumping it without editing the page is a claim the document contradicts. See Recency and authority.

Canonical#

The declared preferred URL for a piece of content. Google consolidates signals onto it; several AI crawlers simply use whatever URL they fetched — which is why a syndicated copy can be the version a model quotes. Covered in The invisible tags.

Semantic HTML#

Markup whose elements state what the content is rather than how it looks. The practical consequence for AEO is the heading outline: a styled div reads as a heading to a person and as a paragraph to a parser. See Semantic HTML for machines.

Retrieval#

Embedding#

A list of numbers representing the meaning of a text as a direction in space — 384 of them for the model in the playground. Texts that mean similar things point in similar directions, which is how a query matches a page with no shared keyword. Explained in How AI search turns your page into numbers.

Vector / vector database#

The embedding itself, and the store that indexes millions of them for nearest-neighbour search. The retrieval half of RAG.

Cosine similarity#

The measure of the angle between two embeddings, from -1 to 1. It ignores length, so a nine-word sentence and a long passage compete on equal terms. Similarity is not relevance — it says two texts are about the same thing, not that either is worth quoting.

Chunking#

Splitting a page into passages that get embedded and retrieved independently. The single most under-appreciated fact in AEO: a retriever returns a chunk, not a page, so any section that only makes sense after reading the intro will be surfaced without it.

RAG — Retrieval-Augmented Generation#

Retrieve relevant chunks, then generate an answer from them. The architecture behind most AI search products, and the reason chunk quality matters more than page quality.

Grounding#

Constraining a model's answer to retrieved sources rather than its trained weights. A grounded answer can cite; an ungrounded one can only assert. Being citable is being useful to grounding.

Hallucination#

A confident, fluent, false statement. Grounding reduces it; clear, self-contained, well-dated content reduces it further, because the model has something specific to hold onto.

Crawlers worth knowing#

The distinction that costs people visibility: blocking a training crawler is a defensible editorial choice with no effect on today's answers. Blocking a search crawler removes you from them. Most blanket rules do both. Sorted out in Why your site doesn't show.

Measurement#

AI Overviews#

Google's generated answer above the organic results. Cites sources, and the citation is often the only visibility a page gets for that query.

Citation#

A named, linked reference to your page inside a generated answer. The unit AEO is actually optimising for.

Zero-click#

A query answered without any visit to a source. The reason impressions can climb while sessions do not, and the reason click-through rate is the wrong success metric for this work.

AEO score#

The 0–100 summary the AEO Checker extension computes for the page you are on, from the parseability signals above. A score, like any score, is a prompt to look — not a result.

FAQ#

What is the difference between AEO, GEO and LLMO?#

Emphasis, not technique. AEO stresses being the source of a direct answer, GEO stresses how you are represented in generated text, LLMO frames the same work model-first. Anyone claiming a methodology exclusive to one of the three is selling a label.

Is llms.txt an actual standard?#

No. It is a proposed convention with real but partial adoption — under 10% of the pages ranking for AEO queries publish one. That is an argument for shipping it, not against: it is a fifteen-minute job, and it is currently the rarest signal in the field.

What is chunking, and why does it matter more than page length?#

Retrieval operates on passages, not documents. Your page is split, each passage is embedded separately, and one passage is matched against the question. A brilliant page whose sections do not stand alone will be retrieved as a fragment nobody can quote. Write each section as though it will be read alone, because it will be.

Does structured data guarantee a citation?#

No. It removes ambiguity about who wrote a page, when, and what it is — which gets you eligible. Whether you are quoted depends on whether the passage retrieved actually answers the question. Structured data is necessary and nowhere near sufficient.

Which term should I use when writing about this?#

Whichever your audience searches. AEO has the most traction among practitioners, GEO among academics and in the papers, AI SEO among marketers. They point at the same body of work, so pick one, define it once, and be consistent.