Generative Engine Optimization
Also called GEO.
Generative Engine Optimization (GEO) is the discipline of optimizing content to be cited by generative AI engines like ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Microsoft Copilot.
What it means
Generative Engine Optimization (GEO) is the term coined alongside the rise of AI-powered answer engines to describe the optimization work specific to those systems. Where classic SEO optimizes for ranking on a page of links, GEO optimizes for being cited inside a generated answer.
GEO and Answer Engine Optimization (AEO) are used interchangeably. GEO emphasizes the engine type (generative AI); AEO emphasizes the outcome (being cited in an answer). Both describe the same practical work: structuring content for citation by LLM-powered systems.
The GEO playbook combines classic SEO foundations (entity strength, content quality, schema markup) with structural elements LLMs preferentially cite: TL;DR boxes, FAQ schema, comparison tables, definitional sentences, citable statistics with sources, and properly-attributed expert quotes.
Key takeaways
- GEO targets the 6 major AI engines: ChatGPT, Perplexity, AI Overviews, Gemini, Claude, Copilot
- Citation rate is the success metric, not ranking position
- Structured data is the highest-leverage technical lever for GEO
- Original research and proprietary statistics get cited disproportionately
When it matters
GEO matters whenever your buyers consult AI engines before clicking through to websites. For most modern B2B and ecommerce funnels, that's already happening. The cost of ignoring GEO compounds — being invisible to engines that intercept queries means lost share of mind even when traditional search rankings hold steady.
- GEO
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AI Overviews
Also: Google AI Overviews, SGE, Search Generative ExperienceAI Overviews are AI-generated summaries that appear at the top of Google search results, synthesizing answers from multiple sources before a user clicks any link.
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Also: AEOAnswer Engine Optimization (AEO) is the practice of structuring content so it gets cited by AI answer engines like ChatGPT, Perplexity, Google AI Overviews, and Gemini — not just ranked on traditional search engines.
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Also: LLMOLLM Optimization is the practice of structuring content and brand signals so Large Language Models — ChatGPT, Claude, Gemini, and the engines they power — preferentially cite and reference your content when generating answers.
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Also: JSON-LD, Microdata, SchemaStructured data is information formatted in a standardized way — typically using the Schema.org vocabulary in JSON-LD format — so search engines and AI systems can understand and use it explicitly.
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