What is GEO?

GEO stands for Generative Engine Optimization. It refers to making a company's reliable information discoverable and identifiable as a source in answers generated by search systems. Distinctions matter: a link in an answer is not necessarily a site visit, and a brand mention without a link is not a qualified enquiry. GEO is not an official Google ranking programme. For Google's AI Overviews and AI Mode, the ordinary Search foundations remain central, particularly indexed pages eligible for snippets and helpful content. A company should first identify the questions its customers actually ask, check whether its pages answer them accurately and make evidence easy to verify. No markup or writing format can guarantee a citation.

SEO covers technical discoverability, content and relevance to search queries. AEO often emphasizes clear answers to specific questions in individual passages. GEO additionally asks whether a source is visible in, or contributes to, generated answers. None of these labels is a standardized technical system, and their practices overlap. Google's AI Overviews and AI Mode build on existing Search systems. A page that cannot be indexed will not become a source simply because its FAQ is concise. For content planning, the reader's intent matters more than the label: a definition belongs in a glossary; a concrete account of scope, process and contact belongs on a service page. That distinction avoids duplicate pages competing for the same informational need.

For a page to appear as a supporting link in Google AI Overviews or AI Mode, it must be indexed and eligible for a Search snippet. There is also a Search Console control for generative Search features: excluding a site there prevents it from appearing in those features; inclusion is the default. Google recommends accessible crawling, relevant internal links, important information in readable text and a useful page experience. Structured data should accurately describe visible content. These are eligibility conditions and quality foundations, not a promise of inclusion. The systems decide for each query whether to show an AI response and which sources to link. Audit technical barriers before producing new content solely to target AI systems.

Neither an llms.txt file nor special AI schema is required for Google's generative Search features. Google explicitly says its Search systems do not treat these special AI text files as an inclusion signal. Splitting a page into artificially tiny passages brings no automatic benefit either. Standard structured data can support appropriate search appearances when it matches visible content, but cannot guarantee an AI Overview link. An existing llms.txt may still serve as an editorial directory for other purposes; any effect on another provider needs independent evidence. Prioritize indexable, understandable pages with traceable sources and useful internal links. Be wary of a vendor who presents a particular file format as a mandatory key to AI visibility.

Define the outcome first: a mention, a visible source link, a visit, an enquiry and revenue are different things. In Google Search Console, AI Overviews and AI Mode contribute to Web search performance. A separate generative AI performance report shows impressions by page, country, date and device. Impressions do not reveal how many qualified contacts resulted, and that report does not cover other answer platforms. Add website analytics and repeatable manual checks. Record platform, language, location, date and displayed links for a stable set of questions, and check whether claims are accurate. Compare several periods under similar conditions. A single screenshot or short-lived fluctuation is not robust evidence of a change in performance.

No. The original Generative Engine Optimization paper studied source visibility in a controlled answer environment and tested interventions including sourcing, quotations and statistics. Its reported improvements concern that experimental setup and its specific visibility measures. They do not imply a guaranteed rise in organic Google rankings or a fixed increase in leads for your business. The researchers also describe limits to generalizing their results and did not establish a general effect on search rankings. A promise that every website will improve by a particular percentage misrepresents the evidence. Treat the study as a prompt to publish verifiable, accurate information, then assess your own visibility, visits and commercial outcomes separately.

Begin with the questions buyers actually raise before a decision: requirements, cost drivers, limitations, alternatives and integrations. Review existing pages for answers grounded in your experience and in verifiable evidence. Substantively update outdated sections rather than publishing a new page for every wording variation. Check indexing, snippet eligibility, relevant internal links and, where applicable, the Search Console setting for generative Search features. Sources, named responsibility and concrete examples help readers evaluate claims, but no team can promise an AI citation. Agree on measurable goals next, then review search performance, qualified visits and enquiries over several comparable periods. Use those findings to decide which existing information needs further improvement.