What is GEO?
GEO stands for generative engine optimization. It is the practice of getting a site's content used and cited inside answers generated by AI systems, whether that is Google's AI Overviews and AI Mode, or an assistant such as ChatGPT, Claude, Perplexity or Grok.
The difference from classic search optimisation is not the tactics but the unit of success. Traditional SEO competes for a position in a list, and the click is the reward. GEO competes to be the passage a model selects when it composes an answer, and the reward is often a citation without a visit. Both matter, and they are not the same game.
The term is used interchangeably with AEO, answer engine optimization. The distinction between the two is largely marketing. What is genuinely useful to separate is the surface: Google's AI Overviews and AI Mode behave as two distinct citation engines, and published analyses of overlapping queries find that the URLs they cite coincide far less often than most people assume. Being present in one does not guarantee presence in the other.
Why GEO matters
- A growing share of questions is answered without a click. When an answer is composed rather than listed, appearing in it is the only form of visibility available. A page ranked third on a query that now produces a generated answer may be read by nobody.
- Citation is a different kind of endorsement. A model naming your company as the source of a claim is closer to a recommendation than a search result is. It arrives with implied authority and without an advertisement label.
- Brand mentions appear to matter more than links. Analyses published across 2025 and 2026 consistently find that being talked about across the web correlates better with citation frequency than traditional link authority does. That reallocates effort from link acquisition towards being quotable and being discussed.
- Freshness carries more weight than in classic ranking. Recently updated content is cited noticeably more often, which changes maintenance from hygiene into an active tactic.
- Most competitors are not doing it. The work is unglamorous and largely invisible in a browser, which is precisely why the gap is still open in French-speaking markets.
The honest caveat belongs here rather than in a footnote. This field is young, its measurement is imperfect, and the figures circulating in the industry come from third-party analyses rather than from platform disclosures. Treat them as direction, not as constants, and be suspicious of anyone quoting them to two decimal places.
How it works
An answer engine does three things in sequence, and each one is a place to intervene.
It retrieves. Either from its index or by fetching pages live. This is why crawlability remains decisive and why server-side rendering matters so much: most AI crawlers do not execute JavaScript, so content assembled in the browser is invisible to them. A page that renders beautifully for a human and returns an empty shell to a fetcher scores zero regardless of its quality.
It selects passages. The model does not quote a page, it quotes a fragment. Fragments that survive extraction are self-contained: they answer one question completely, define their own terms, and contain no pronoun pointing at a paragraph the reader can no longer see. Analyses of cited passages find that a disproportionate share comes from the opening portion of a page, which argues for answering the question early rather than building up to it.
It attributes. The system decides which source to name. Signals that help are the ones that resolve identity and accountability: structured data describing what the page is about, a named human author with a verifiable profile, visible publication and update dates, and outbound links to primary sources for factual claims.
Implementation
- Serve your content in the HTML. Check what a fetcher actually receives rather than what the browser displays. This single item disqualifies more pages than every other factor combined.
- Write one self-contained answer near the top of every page. Roughly a paragraph, complete on its own, with no reference to what came before it. That block is the candidate for extraction.
- Answer the question in the heading. A heading phrased as the question a person would ask makes the passage below it easier to match than a clever title does.
- Attribute your facts and link the sources. A claim with a linked primary source is more quotable than the same claim asserted, because the model can corroborate it.
- Name a human author and show the dates. Author identity and a visible update date are cheap to implement and are among the clearest accountability signals available.
- Add structured data that describes the entity. Not for a rich snippet, but so the machine knows what the page is about and how it connects to the rest of the site.
- Update deliberately and record it. Refresh when something has genuinely changed, and move the date then. Moving a date without changing anything is a false signal that eventually costs more than it earns.
- Measure by prompt, not by keyword. Keep a list of the questions your buyers actually ask, run them across the engines on a schedule, and record whether you are cited. There is no rank tracker for this yet, so the corpus of prompts is the measurement instrument.
Related technologies and tools
- Structured data: schema.org vocabulary describing what a page is about, which entities it concerns and how they relate, so attribution has something to attach to.
- Server-side rendering: delivering complete HTML from the server, which is what makes content visible to crawlers that do not run JavaScript.
- AI Overviews and AI Mode: Google's two generative surfaces, which behave as separate citation engines and need to be checked separately.
- Prompt monitoring: tracking whether a defined set of buyer questions produces a citation, across several engines, which is the closest thing to rank tracking that currently exists.
- llms.txt: an emerging convention for pointing AI systems at a site's most useful content. Cheap to publish, and adoption by the engines is still uneven, so treat it as an option rather than a requirement.
Conclusion
GEO is less a new discipline than a change in what counts as a win. The technical foundations are the same ones good SEO always required, crawlability, clear structure, real expertise, and they are non-negotiable because a page that cannot be read cannot be quoted. What is new is that the passage becomes the unit of competition, and that a citation without a click becomes a legitimate objective rather than a failed one.
The practical consequence for a mid-sized company is encouraging. The work is writing self-contained answers, attributing facts, naming authors, showing dates and serving real HTML. None of it requires a budget, all of it requires discipline, and most competitors have not started. The one thing to insist on is measurement: without a list of the questions your buyers ask and a habit of checking whether you appear in the answers, GEO stays an opinion.
