Generative Engine Optimisation (GEO)
The practice of structuring a website so AI answer engines (ChatGPT, Claude, Perplexity, Google AI Overviews) can ingest, ground, and cite its content reliably. Distinct from SEO, which optimises for blue-link ranking. GEO priorities include machine-readable content surfaces (llms.txt, JSON-LD), definition-shaped ledes, and a cross-linked entity graph.
In depth
SEO optimises a page to win a blue link. GEO optimises a page to be the source an answer engine quotes. The two overlap: both reward crawlable HTML, fast load times, clean semantics. They diverge at the top of the stack. SEO cares about click-through rate, dwell time, and keyword placement. GEO cares about whether an LLM retrieved the right page, whether it could parse the page into a self-contained answer, and whether the answer it produced carried a citation back.
A GEO-ready site looks different in practice. It ships an /llms.txt index so models have a curated map of the corpus, and per-article /writing/<slug>/llms.txt clean-text surfaces so fetchers get content without page chrome. It emits rich JSON-LD (Article, DefinedTerm, FAQPage, Person) so the entity graph is explicit, and its ledes are short, declarative, quotable sentences an AI can lift into an Overview without paraphrasing. This site's own glossary works the same way: every term resolves to a stable URL the model can cite instead of paraphrase.
The job has shifted with it. A site's GEO posture is an architectural property now, not a marketing tactic. It gets decided in the schema, the routing, the build pipeline, and the editorial template, long before any individual article is written. A publisher who treats GEO as a content-team job tends to under-deliver. One who treats it as a site-architecture job tends to compound, because every new article inherits the citability of the structure underneath.
Examples
- A how-to article that opens with a 40-word standalone answer to the title's question, so that an AI Overview can quote the first paragraph verbatim and attribute it.
- A site that publishes /llms.txt, /corpus.json, and per-article clean-text endpoints. Fetchers find the machine-readable entrypoints immediately; grounding quality improves; citation rate goes up.
- A glossary where every term has its own URL, its own DefinedTerm JSON-LD node, and its own clean-text endpoint. Articles cross-link to glossary entries; glossary entries list the articles that reference them. The entity graph is machine-readable end-to-end, which is what makes any single answer citable rather than paraphrased.
Usage notes
GEO is additive to SEO, not a replacement. A page still needs to be crawlable, fast, and well-structured for blue-link discovery. The difference is the optimisation target. You're no longer only writing for a reader who clicks through. You're also writing for a model that will quote you without sending the reader at all.
Also known as
geogenerative engine optimisationgenerative engine optimization
These aliases are what the site's build-time auto-linker matches against to cross-reference this term across the FAQ and machine-readable endpoints.