Generative engine optimization (GEO) is the work of improving how a business and its content show up in answers written by AI systems. It includes making information easy to find and use, supplying evidence an answer can rest on, and measuring whether the result is a correct, useful appearance — not just any appearance.
If a buyer asks an assistant about a problem you solve, does the answer include you, describe you correctly and send them somewhere useful? GEO is the practice of making that more likely and knowing whether it worked.
#Where the term comes from
The phrase was formalised in a 2023 research paper, GEO: Generative Engine Optimization (Aggarwal et al.), accepted at KDD 2024. The authors tested ways to change a source's visibility inside generated answers, rather than its position in a list of links, and reported improvements of up to roughly 40% on their visibility metrics in their experimental setting.
That result is useful as evidence that content choices can matter. It isn't a promise that any tactic will lift your visibility by a fixed percentage on today's live products. Treat it as a hypothesis to test against your own questions.
#GEO, AEO and SEO
Three terms get used interchangeably:
- SEO — making content discoverable and useful in search.
- AEO (answer engine optimization) — work aimed at being the source of direct answers, including featured snippets and assistant responses.
- GEO — work aimed at generative systems that compose answers from retrieved sources.
They overlap heavily and aren't three independent switches. The pragmatic view: SEO fundamentals — crawlability, relevance, authority, clear pages — remain the foundation, and GEO adds new questions about what answers say and cite.
#Three questions that organise the work
Rather than a checklist of tactics, ask three questions about each important topic.
#1. Can it be found?
Can the relevant systems reach your pages at all?
- Crawler access. Search, training and user-triggered bots are different permissions. See the AI crawler access guide.
- Indexability. Canonicals, response codes and noindex directives still apply.
- Raw HTML. Many retrieval systems read what the server returns without running scripts. Key content that appears only after JavaScript runs may never be seen.
#2. Can it be trusted?
Would an answer feel safe resting on this page?
- Specifics over adjectives. "Supports SSO via SAML, requires the Business plan" is more useful than "enterprise-ready".
- Corroboration. Claims repeated by independent sources are stronger than claims made only on your own site. Review third-party descriptions of your company and correct outdated ones through legitimate channels.
- Freshness and ownership. Dates, authors, versions and a clear owner for each page.
#3. Can it be quoted?
Could a system lift a passage and have it stay true?
- Answer-first structure. An opening paragraph that states the answer, the audience and the key limit, as in answer capsules.
- Sectioned content. Headings that match the questions people ask.
- Tables and lists with inclusions and exclusions rather than marketing summaries.
#What you cannot control
- Which pages a system chooses to retrieve for a given question.
- Whether an answer cites you even when you're retrieved. Retrieval, selection and citation are separate decisions.
- Day-to-day variance: the same prompt can return different brands on different runs.
- The product's behaviour next quarter. Documentation and behaviour change; re-check.
Be wary of anyone who promises a guaranteed citation position. A promise like that needs a defined question, surface, time and verification method to mean anything.
#Do you need special files or markup?
For Google's search experiences, Google's guidance says no special AI text files or GEO-specific markup are required. Other systems differ. llms.txt is a proposed convention that not every engine honours. Evaluate any proposed technical change against the documentation of the system you want to reach, not against a blog post's claim.
#How to measure it
Start with separate measurements rather than one unexplained score:
- Mention rate — valid answers that name the brand ÷ valid answers.
- Owned-citation rate — answers that link to your verified domains ÷ valid answers.
- Recommendation rate — answers that explicitly recommend you for the prompt's use case.
- Source coverage — which independent domains and URLs appear.
- Referral outcomes — identifiable visits and conversions, measured separately.
Define the denominator, prompt set, engines, location and time window. Report failed or unavailable observations separately rather than counting them as absence. See how to measure AI visibility for a repeatable workflow.
#A realistic first month
- Week 1: baseline a fixed prompt panel for one business area.
- Week 2: separate access problems from content gaps; look at the sources.
- Week 3: ship one bounded improvement.
- Week 4: re-measure under comparable conditions and write down what happened.
The full plan has the detail. It's a workflow, not a promise that four weeks proves an effect.
#FAQ
Does GEO replace SEO? No. Keep search fundamentals and add measurement of what answers say and cite.
Can I guarantee ChatGPT will cite my site? No. Access and useful content do not guarantee selection for a particular answer.
How long does GEO take? There's no evidence-backed universal timetable. Set review dates around observable milestones.
#Sources
- Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024.
- Google Search Central, Optimizing for generative AI features in Search.
- OpenAI, Overview of OpenAI crawlers.
- Perplexity, Perplexity crawlers.
- llms.txt proposal.