AI SEO glossary

The AI SEO field is full of overlapping acronyms. These are plain, self-contained definitions you can actually use, updated as the vocabulary settles.

Last updated 22 September 2026By Nayanaba Gohil

AI SEO

Artificial intelligence SEO

AI SEO is the umbrella practice of earning visibility inside AI-powered search and answer engines, and of using AI tools within an SEO workflow. It spans GEO, AEO and LLMO rather than naming a single technique.

GEO

Generative engine optimization

Generative engine optimization is the practice of structuring content so it is retrieved and cited by generative models such as ChatGPT, Perplexity and Google AI Overviews. The term comes from a 2023 research paper later presented at KDD 2024.

AEO

Answer engine optimization

Answer engine optimization is the practice of making a page the direct, extracted answer to a question. It carries over from the featured-snippet era and overlaps heavily with GEO.

LLMO

Large language model optimization

Large language model optimization is a newer label for shaping content so large language models surface and cite it in their answers. In practice it is used interchangeably with GEO.

AIO

Artificial intelligence optimization

Artificial intelligence optimization is another umbrella label for optimizing content for AI-driven discovery. Like the other acronyms, it has no single agreed definition as of 2026.

RAG

Retrieval-augmented generation

Retrieval-augmented generation is the method behind most answer engines: the system retrieves relevant passages from an index or the live web, then a language model writes an answer grounded in those passages. Content that is easy to retrieve is easier to cite.

Google AI Overviews

AI-generated answers in Google Search

Google AI Overviews are AI-generated summaries shown at the top of some Google results, with links to supporting sources. They launched in the United States in 2024 and expanded to many countries afterwards.

AI Mode

Conversational search in Google

AI Mode is Google's conversational search experience, introduced for testing in 2025. Google describes it as using a query fan-out across subtopics and data sources to build an answer.

Query fan-out

One query becomes many

Query fan-out is when a search system expands a single question into several related sub-queries, runs them, and synthesizes the results into one answer. Broad, well-structured coverage of a topic helps you appear across those sub-queries.

Chunking

Passage-level retrieval

Chunking is structuring content into clear, self-contained passages that a retrieval system can pull and quote on their own. Answer engines retrieve passages, not whole pages, so each chunk should make sense in isolation.

Entity-based SEO

Optimizing for things, not strings

Entity-based SEO optimizes for the real-world things a page is about and connects them to known nodes in the Google Knowledge Graph, rather than repeating keywords. It helps machines understand meaning and relationships.

Nested JSON-LD schema

Structured data that links entities

Nested JSON-LD schema is structured data written in JSON-LD where objects reference one another, describing the relationships between entities on a page (for example a Course taught by a Person published by an Organization) rather than isolated facts.

Knowledge Graph

Google's entity database

The Google Knowledge Graph is a database of real-world entities and the relationships between them. Being represented as a recognized entity within it helps search and AI systems understand and trust a topic.

Search UX

User experience of a search visit

Search UX is the experience a visitor has after clicking a result: how fast the page loads, how quickly it answers, and how little friction stands between the question and the answer. Poor Search UX raises bounce and undermines rankings.

Ready to put these into practice?

Find your level and get a learning path that turns these terms into real skills, from foundations to generative engine optimization.

Find your level