Generative engine optimisation: what it is and how it relates to SEO
TL;DR
GEO, AEO, and LLMO all describe a similar idea: making your content more likely to appear in AI-generated search results. The terminology is still settling, but the underlying work overlaps heavily with quality-focused SEO.
If you work in search marketing, you have probably encountered the term GEO in the last year or two. You may also have seen AEO and LLMO. All three describe a similar idea: making your content more likely to appear in AI-generated search results. The terminology is still settling, but the underlying work is not as new as the acronyms suggest.
This post explains what generative engine optimisation means, how it overlaps with traditional SEO, and what is actually worth doing differently.
What generative engine optimisation means
Generative engine optimisation (GEO) is the practice of optimising content to be cited in AI-generated answers from search surfaces like Google AI Overviews, Google Gemini, Microsoft Copilot, ChatGPT, Perplexity, and Claude. Where traditional SEO targets position in a ranked list of links, GEO targets inclusion in a synthesised answer.
The term was formalised in an academic paper by Aggarwal et al. (arXiv 2311.09735, published at KDD 2024). The researchers tested nine content optimisation tactics across 10,000 queries and measured their impact on visibility in AI-generated responses. Three tactics stood out:
- Quotation addition: +41% visibility improvement
- Statistics addition: +32%
- Citing sources: +30%
The study used a custom retrieval system built on GPT-3.5-turbo with Google search retrieval, and validated results on Perplexity.ai, where improvements reached up to 37%. Keyword stuffing had a negative effect (-8%).
The methodology relied on a controlled benchmark, not live production AI surfaces. The authors acknowledge that tactics may need to adapt as generative engines evolve, and that effects on traditional search rankings were not evaluated.
Three names for overlapping work
GEO is not the only acronym in this space.
[AEO](/tools/glossary?term=aeo--answer-engine-optimisation-) (Answer Engine Optimisation) focuses on appearing as direct answers in AI-powered search results and chatbots. The term predates GEO and has broader adoption among SEO practitioners.
[LLMO](/tools/glossary?term=llmo--large-language-model-optimisation-) (Large Language Model Optimisation) targets standalone LLM chatbots specifically (Google Gemini, ChatGPT, Claude, Microsoft Copilot as conversational tools) rather than AI features embedded in traditional search engines. The term was introduced by Olaf Kopp in a Search Engine Land article in October 2023.
[GEO](/tools/glossary?term=geo--generative-engine-optimisation-) sits between the two, targeting generative features within search engines (like Google AI Overviews) specifically.
In practice, the differences are more taxonomic than tactical. Lily Ray, VP of SEO strategy and research at Amsive, put it directly in an eMarketer piece from April 2026: "The overlap with what we've been doing in the SEO space and digital marketing space before AI search existed is very, very strong."
Ryan Law, Director of Content Marketing at Ahrefs, made a similar argument in August 2025, noting that the three main paths to LLM visibility (training data, retrieval from search indexes, and adversarial manipulation) all reduce to "just SEO" or "just black hat SEO." His challenge ("can anyone show me a company who has great visibility in LLMs but not in traditional search?") remains largely unanswered.
How GEO relates to traditional SEO
The overlap is substantial.
Google's September 2025 quality rater guidelines update added examples for evaluating AI Overviews and clarified YMYL definitions. While Google characterised it as a minor update, secondary analysis suggests the same E-E-A-T quality signals that raters apply to traditional results are being used when evaluating AI Overview content.
Seer Interactive studied 3,119 search terms across 42 client organisations over 15 months (June 2024 to September 2025) and found that brands cited in an AI Overview had 35% higher organic CTR (0.70% vs 0.52%) compared to non-cited brands on the same queries. The sample covered 25.1 million organic impressions.
But the overlap is not total. An Ahrefs study of 15,000 long-tail queries (September 2025) found that only around 8% of Gemini, ChatGPT, and Copilot citations came from pages ranking in Google's top 10 for the equivalent query. More than 80% of their citations came from pages that did not rank at all. Perplexity was an outlier at 28.6% overlap, pulling the overall average to 12%.
Separate research from Profound (formerly Otterly), based on 100,000 prompts, found only 11% domain citation overlap between ChatGPT and Perplexity, meaning 89% of the domains cited by one platform were not cited by the other.
The picture that emerges: the foundation (content quality, authority, structured information) is shared between traditional SEO and GEO. But AI surfaces select and synthesise from a broader pool than the top 10 organic results, and each platform draws from different sources. Ranking well on Google helps, but it does not guarantee visibility across Google AI Overviews, Gemini, ChatGPT, Copilot, Claude, or Perplexity.
What is worth doing in practice
The Aggarwal et al. findings and the citation research point in a consistent direction. None of this requires abandoning your existing SEO work, but there are areas worth reviewing.
Structure content for extraction. AI systems pull specific passages rather than evaluating whole pages. Answer-first formatting, clear heading hierarchies, and self-contained paragraphs that work as standalone answers make your content easier to cite. This is the same advice that improves featured snippet eligibility.
Strengthen entity signals. Consistent information about your organisation and authors across your website, Google Business Profile, and directory listings helps AI systems identify and trust your brand. Schema markup (Organization, Person, LocalBusiness) provides machine-readable entity data. The "entity SEO" concept predates GEO but becomes more relevant as AI surfaces rely on entity recognition to select sources.
Include citable specifics. The Aggarwal et al. study found the largest visibility improvements from content that included quotations (+41%), statistics (+32%), and cited sources (+30%). AI systems appear to favour content with concrete, attributable details over general summaries. Original data, named sources, and specific figures give AI surfaces something to reference and attribute.
Use structured data where appropriate. FAQ, HowTo, and Article schema improve machine readability. This benefits traditional rich results and AI citation simultaneously.
Monitor your AI visibility. There is no standard measurement methodology yet. Different AI surfaces cite different sources for the same query (Profound, 2025). Periodically checking whether your brand appears in answers from Google AI Overviews, Gemini, ChatGPT, Claude, Copilot, and Perplexity for your core queries is worth doing, though the tooling for systematic tracking is still emerging.
What we still don't know
The Aggarwal et al. study tested tactics in a controlled benchmark environment. The visibility improvements (41%, 32%, 30%) are relative to a baseline in that environment, not guarantees of real-world performance across all AI surfaces.
The citation overlap data from Ahrefs and Profound shows that AI surfaces draw from a much wider pool than Google's top results. But neither study fully explains what determines citation selection. The ranking factors for AI citation are not published the way Google's traditional ranking signals are discussed.
Liz Reid, VP and head of Google Search, described the priority as content from "someone who really went in and brought their perspective, or brought their expertise, put real time and craft into the work" (WSJ "Bold Names" podcast, October 2025, via Search Engine Land). That framing is consistent with E-E-A-T principles, but it is a qualitative statement, not a ranking algorithm specification.
Where this leaves the terminology
The taxonomy will likely consolidate. AEO, GEO, and LLMO describe the same strategic shift viewed from different angles: answer engines, generative search features, and standalone LLM chatbots respectively. Whether a practitioner optimises "for GEO" or "for AI search" or simply does thorough, well-structured, evidence-based SEO, the work is largely the same.
The Aggarwal et al. research gave the field an academic foundation and a useful name. The practical reality, as both Lily Ray and Ryan Law have observed, is that most of the work is an extension of quality-focused SEO. The specifics worth paying attention to are citation-ready formatting, entity authority, and the emerging differences in how each AI surface selects its sources.