Generative engine optimisation, or GEO, is the practice of publishing content so that AI systems which generate answers — rather than list links — can understand it, trust it and reference it accurately. It covers answer-first writing, clear structure, explicit entity naming, verifiable sourcing and internal consistency. Most of these practices predate the term.
Where the term comes from
The phrase emerged as AI-generated answers began appearing in search results and as assistants became a route by which people find information. It names a real shift: when a system reads pages and composes an answer, the objective changes from being ranked to being used.
It is not yet a settled term. "AI search optimisation", "answer engine optimisation" and "LLM optimisation" are all in circulation for overlapping ideas, and the boundaries between them are drawn differently by whoever is doing the drawing. That instability is worth keeping in mind when the term appears in a proposal with a price attached.
What GEO actually asks you to do
- Answer first
- State the answer in the opening paragraph and explain afterwards. A system looking for an answer starts at the top.
- Write self-contained paragraphs
- Each paragraph should make sense removed from its page, which means naming its subject rather than referring back.
- Name entities explicitly
- Organisations, people, products and places, stated consistently across the site.
- Structure facts as facts
- Tables for specifications, lists for sets, definitions for terms. Prose hides data that structure exposes.
- Show your sources
- Named, linked, dated. A claim nobody can check is a claim nothing should repeat.
- Be internally consistent
- The same fact, the same way, everywhere. Contradictions make the whole source less usable.
- Publish something original
- Data, research, testing or genuine expertise. A summary of what everyone else already said has no reason to be selected over them.
What GEO is not
- Not a ranking technique. There is no position to win and no scoreboard to check.
- Not a guarantee of citation. No one can promise inclusion in any AI system's answers, and the systems themselves do not publish how sources are chosen.
- Not keyword stuffing for machines. Writing for extraction means being clearer, not being more repetitive.
- Not a replacement for SEO. The two overlap heavily, and abandoning search work in favour of this would be an odd trade.
- Not achievable by publishing a file. Machine-readable discovery files such as llms.txt are cheap and possibly helpful; they are not a mechanism for inclusion.
How to tell good GEO advice from the rest
The field has attracted a considerable amount of confident advice with very little evidence behind it. A few tests help.
- Does it promise a specific outcome — citation by a named system, a number of mentions? Nobody can deliver that.
- Does it claim precise measurement of AI visibility? Very little of this is observable, and the honest practitioners say so.
- Does the advice also improve the page for people? If a recommendation only makes sense for a machine, be suspicious.
- Does it depend on an unproven file or protocol being universally honoured? Emerging conventions are worth adopting cheaply, not building a strategy on.
- Would it still be sensible if the term GEO disappeared? The durable practices would.
Questions
What is the difference between GEO and AEO?
Answer engine optimisation (AEO) is the older term and originally described optimising for featured snippets and voice assistants — being the answer rather than a result. Generative engine optimisation (GEO) describes the same objective for systems that generate their answers rather than extracting them. In practice the recommendations largely coincide.
Is GEO worth paying for as a separate service?
It is worth doing. Whether it is worth buying separately depends on the state of your site: most sites need a structural review and a set of specific fixes, not an ongoing retainer for a discipline whose results cannot currently be measured.