What is GEO?

GEO (Generative Engine Optimization) is a strategy whose goal is being cited and surfaced inside the answers produced by LLM-based generative engines.
Its objective differs fundamentally from SEO's pursuit of rankings: the unit of success is not a page's position but its visibility within the answer.
The concept was first formalized in the paper GEO: Generative Engine Optimization by Aggarwal and six co-authors (arXiv:2311.09735, KDD 2024).
That same paper demonstrated experimentally that GEO techniques can raise visibility in generative-engine responses by up to 40%.
Adding citations, quotations from sources, and statistics proved especially powerful, lifting visibility by more than 40% on average.

GEO (Generative Engine Optimization) is the practice of making your content easy for a generative engine to cite and surface when it builds an answer. Generative engines such as ChatGPT, Perplexity, and Google's AI Overviews and AI Mode use an LLM to synthesize information from many sources into a single response. Where traditional search returns a ranked list of links, a generative engine evaluates and summarizes information on the user's behalf and presents one finished answer. In that answer your brand is either included or it is not — there is no middle ground. GEO is the strategy for landing on the included side.

The concept was first formalized academically in GEO: Generative Engine Optimization by Pranjal Aggarwal and five co-authors (arXiv:2311.09735), published at KDD (ACM SIGKDD) in 2024. The paper unifies systems that synthesize and summarize search results under the single definition of a "generative engine" (GE) and proposes GEO as the first framework to help content creators improve their visibility inside these black-box engines.

How does GEO differ from SEO?

GEO does not replace SEO; it is a complementary strategy that arises because the surface being evaluated has changed. The key differences are as follows.

Dimension	SEO	GEO (Generative Engine Optimization)
Goal	Rank high on the search engine results page (SERP)	Get cited and surfaced inside the generative engine's answer
Output form	A ranked list of links	A single answer synthesized from many sources
Unit of optimization	Page- and keyword-level rankings	Sentence- and source-level visibility within the answer
Primary surfaces	Google and Bing search results	ChatGPT, Perplexity, Google AI Overviews and AI Mode
Success metrics	Rankings, click-through rate (CTR), organic traffic	Frequency of AI citations and mentions, share of visibility within answers

The two disciplines share much of the same groundwork — crawlability, trustworthy sources, clear structure — but "climbing the rankings" and "appearing in an AI-generated answer" are distinct outcomes, and neither guarantees the other.

How do generative engines build an answer?

Understanding GEO requires understanding how generative engines work. According to Google's official documentation, AI Overviews and AI Mode use a technique called query fan-out, issuing multiple distributed searches across related subtopics and data sources for a single query. Because the model identifies additional supporting web pages while generating its response, a broader and more varied set of sources can be linked into the answer than in classic web search. In other words, a generative engine is not judging the ranking of a single page — it splits the query into several branches and gathers trustworthy evidence to answer each one. This is precisely why GEO should focus on providing evidence that answers the question rather than on keyword rankings.

The evidence: what actually raises visibility?

The GEO paper (arXiv:2311.09735) quantitatively evaluated several optimization techniques using GEO-bench, a benchmark of queries spanning a range of domains. The main findings were:

Applying the right GEO techniques raised visibility in generative-engine responses by up to 40%.
The strongest effect came from content-quality signals. The paper reports that "including citations, quotations from relevant sources, and statistics significantly improved source visibility, increasing it by more than 40% across several queries."
The effectiveness of each technique varied by domain, indicating that domain-specific optimization is necessary.

These results suggest that content that backs its claims with evidence (sources, quotations, and statistics) gets cited more readily by generative engines than keyword-stuffing tactics do.

GEO execution checklist

Frame the core question as a subheading (H2/H3) and give a clear, one-to-two-sentence answer directly beneath it.
Support every claim with trustworthy citations, quotations, and concrete statistics.
Include first-person experience, hands-on measurements, and original research so that real experience and expertise (E-E-A-T) come through.
Use lists, tables, and step-by-step explanations to create structure that machines can easily extract.
Signal the meaning and freshness of your content clearly with structured data (JSON-LD) and fields such as dateModified.
Verify that AI crawlers can access your pages and that the rendered body is exposed as text.
Regularly query ChatGPT, Perplexity, Gemini, and others with questions about your topic to monitor whether you are cited or mentioned.

Sources

Related concepts include AEO, LLMO, AI Overviews, AI citations, Share of Model, query fan-out, structured data, and SEO.