GEO (Generative Engine Optimization) is the practice of optimising content so that AI systems such as ChatGPT, Perplexity or Google AI Overviews cite it as a source in their answers. GEO complements classic SEO with factors such as quotable statements, source citations, structured data and machine-readable content.
More and more people no longer ask a classic search engine – they ask an AI assistant and receive a fully formulated answer instead of a list of links. This is exactly where GEO comes in: while classic SEO aims to rank as high as possible in search results, Generative Engine Optimization is about making AI systems understand your content, classify it as trustworthy and name it as a source in their answers.
The term was coined in 2023 by researchers from Princeton University and Georgia Tech. Their key finding: with targeted optimisation techniques, the visibility of content in AI answers can be increased by 30 to 40% (Princeton University). The topic is gaining relevance due to a shift in search behaviour – Gartner forecasts that traditional search queries will decline by 25% by 2026 (Gartner), while AI-powered answer systems such as Google AI Overviews are reaching a mass audience.
GEO vs. SEO: what is the difference?
SEO and GEO are not mutually exclusive – they set different priorities. SEO optimises for ranking algorithms: keywords, backlinks, technical performance and user signals determine the position in the results list. GEO optimises for language models that evaluate content semantically and assemble answers from it. AI systems favour content that makes precise statements, backs up claims with sources, is clearly structured and makes it obvious who is behind the information. A page can rank exceptionally well and still barely appear in AI answers – and vice versa.
The most important GEO measures
- Quotable statements: Precise facts, figures with source citations and clear definitions instead of vague marketing copy
- Question-and-answer structures: Content that answers concrete user questions directly and concisely – for example FAQ sections or succinct short answers at the top of the page
- Structured data: Schema.org markup (FAQ, Article, Organization) that makes content machine-readable
- Clean content structure: Meaningful headings, lists and tables that language models can extract easily
- Clear entities: Unambiguous information about who the provider is and what expertise they can demonstrate
- Machine-friendly delivery: Content that is accessible without JavaScript barriers, complemented by formats such as llms.txt
Generative search systems still rely heavily on classic search indexes: content that cannot be found organically is, as a rule, not cited by AI systems either. GEO is therefore an extension of good SEO work, not a replacement for it.
The topic is particularly urgent for online retailers: Google AI Overviews already appear for 87% of all e-commerce search queries (Onely Research). Product data, category texts and guide content that is not prepared in a machine-readable way simply does not appear in these answers. Service providers benefit as well: being named as a source in AI answers reaches potential customers at a moment when classic ads are not even displayed. Measuring success is still in its early stages – what can be monitored includes mentions of your own brand in AI answers and visitors arriving on your website via AI assistants.
For businesses this means: the fundamentals of good search engine optimisation – a technically sound website, high-quality content, clear structure – remain the foundation. Building on that, it pays to make content deliberately quotable and to monitor your own visibility in AI answers. AI-supported processes within your own company – for example for content creation or data enrichment – can also speed up implementation considerably.