GEO / AEO
Generative Engine Optimization / Answer Engine Optimization — optimizing for AI answer systems.
GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) are the practices of optimizing content to be cited by AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini. The tactics are related to traditional SEO but emphasize answer-first structure, entity density, sourced statistics, and schema markup.
Context
The Princeton GEO study (KDD 2024) ranked 9 optimization methods by their effect on citation visibility across Perplexity: citing sources (+40% visibility), adding statistics (+37%), adding quotations (+30%), authoritative tone (+25%). Keyword stuffing actively reduces AI citation by -10%.
AI systems select sources differently from organic search. A page ranking organically on page 2 can be cited more often in AI answers than a page ranking 1st, because AI selection weights extractability and answer-completeness alongside authority.
Comparison content ('[X] vs [Y]') and definitive guides are cited far more often by AI systems than listicles. Structured definition-context-example blocks get extracted verbatim; chatty intros don't.
GEO is not a replacement for SEO. It's a layer on top. Authority, links, and traditional signals still matter — they just aren't sufficient for AI citation without the structural adjustments.
Related terms
Services that apply this
More AI Marketing terms
LLM (Large Language Model)
A neural network trained to generate human-language text from vast training data.
RAG (Retrieval-Augmented Generation)
An LLM architecture that looks things up before answering, rather than relying solely on training data.
Prompt Engineering
Designing the text instructions given to LLMs to produce reliable outputs.
AI Agent
An AI system that uses tools and takes multi-step actions to achieve a goal.