What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the academic and industry term for optimizing content to be cited and quoted by generative AI systems. The term was coined in a 2023 paper from researchers at Princeton, Georgia Tech, and Allen Institute for AI (Aggarwal et al., "GEO: Generative Engine Optimization"), which empirically tested nine optimization tactics across BingChat and Perplexity. The paper found that adding citations, quotations from authoritative sources, and statistics increased citation share by up to 40%.

GEO and AEO are used interchangeably in practice, though GEO emphasizes the generative side (how LLMs synthesize answers) while AEO emphasizes the retrieval side (which sources get pulled into the context window). Most platforms — including Surfaced — cover both because the workflows overlap: you can't be quoted if you aren't retrieved, and being retrieved without being quotable is a dead end.

The core GEO playbook from the original paper plus 18 months of practitioner data: (1) include direct, citable statistics with year stamps, (2) quote named experts, (3) use clear declarative sentences in the first 100 words of each section, (4) implement Article, FAQPage, and Organization schema, (5) build entity coverage on Wikipedia and Wikidata, (6) earn third-party citations on Reddit, Wikipedia talk pages, and high-DR publications, and (7) refresh content quarterly so timestamps stay current.