What is Generative Engine Optimization (GEO)?

Where the term comes from

The term was introduced in the research paper "GEO: Generative Engine Optimization" by Aggarwal et al. (Princeton University and collaborators), first published on arXiv in November 2023 (arXiv:2311.09735) and presented at KDD 2024. The paper framed generative engines as systems that retrieve sources and synthesize one answer, and studied how content changes affect a source's visibility in those answers.

How GEO differs from SEO

SEO aims for a high position in a ranked list of links. GEO aims for inclusion inside a single synthesized answer: being named (a mention), being linked as a source (a citation) and being described accurately and favorably. A page can rank well in Google and still be absent from AI answers, and the reverse.

What GEO work involves

Typical GEO work includes: tracking a fixed set of prompts across AI engines; checking which sources engines cite for your topics; making pages easy to retrieve and quote (clear answers, facts, structure, schema); letting AI crawlers access your site; and earning mentions on the third-party sites engines cite, such as reviews, forums and comparison pages.

How GEO is measured

GEO tools run prompts on a schedule and report AI visibility (how often you appear), citation share, share of voice against competitors, position in the answer and sentiment. Results vary between runs because AI answers are non-deterministic, so trends over many prompts matter more than single answers.

GEO vs AEO vs LLMO

Answer Engine Optimization (AEO) and LLM Optimization (LLMO) are near-synonyms. AEO is the older term, originally used for featured snippets and voice answers; GEO is used most often for generative AI answers. In practice vendors use them interchangeably.

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