Glossary
The language of modern search, in plain terms.
Short definitions for the terms that come up in AI search work. No jargon for its own sake, and no inflated claims about what any of it guarantees.
(01) Core disciplines
Core disciplines
- SEOSearch Engine Optimization
- Improving visibility in traditional organic search results through crawlability, content quality, authority, and technical health.
- AEOAnswer Engine Optimization
- Structuring information so search and answer systems can understand it and surface direct answers, through question-and-answer structure, structured data, and content written to be extracted, not just read.
- GEOGenerative Engine Optimization
- Improving the likelihood that generative AI systems retrieve, reference, and cite your brand or content in synthesized responses.
(02) How AI systems find answers
How AI systems find answers
- AI Overviews
- Google's AI-generated summaries that appear above traditional results, synthesizing information from multiple sources with links to the pages used.
- Answer engine
- A search experience that responds with a direct, often AI-generated answer instead of only a list of links, such as ChatGPT, Gemini, or Perplexity.
- LLM citation
- A reference to a specific source inside an AI-generated response. Citations are becoming a measurable form of search visibility in their own right.
- Query fan-out
- When a single question typed into an AI search system is expanded into many related sub-queries behind the scenes. Your content needs to answer the full set, not just the original wording.
- RAGRetrieval-Augmented Generation
- A pattern where an AI system first retrieves relevant documents, then generates an answer grounded in them. Being retrievable is the prerequisite for being cited.
- Zero-click search
- A search that ends without a click because the answer was delivered on the results page itself. Increasingly common as AI summaries expand.
(03) Making content machine-readable
Making content machine-readable
- Answer-first content
- Content that leads with the direct answer, then supports it with depth and evidence. Written to be extracted as well as read.
- Entity optimization
- Making your brand, products, and people unambiguous to machines, so systems correctly understand who you are, what you offer, and how you relate to other entities.
- Structured dataSchema markup
- Standardized markup that describes a page's content to machines, helping search engines and AI systems understand entities, relationships, and facts.
- llms.txt
- A proposed file at the root of a website that gives AI assistants a curated map of its most important content, similar to how robots.txt speaks to crawlers.
(04) Where to go next
Definitions are the start. Application is the work.
Knowing the terms is one thing. Knowing where your brand stands across these surfaces, and what to do about it, is another.
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