GEO

GEO Glossary

A quick reference to the core concepts of Generative Engine Optimization (GEO) — from GEO, AEO, and Share of Model to RAG, structured data, and entity linking.

GEO (Generative Engine Optimization): The practice of optimizing a brand so that generative AI engines (ChatGPT, Gemini, Claude, Perplexity, and others) recommend, cite, and accurately describe it in their answers.
SEO (Search Engine Optimization): Optimizing pages to rank higher in traditional search engines (Google, Bing, Baidu) where users click through links. GEO extends SEO to AI-generated answers.
AEO (Answer Engine Optimization): Optimizing content to win featured snippets and direct answer cards by structuring Q&A content and FAQ schema. AEO is a subset of GEO focused on the answer surface.
GEO Score: XOOER's proprietary brand-visibility score that quantifies a brand's authority across major AI models, combining Share of Model, ranking, citation, sentiment, and accuracy.
Share of Model (SOM): A brand's share of AI recommendations for a given query set — how often it appears versus competitors when AI models answer category questions.
RAG (Retrieval-Augmented Generation): A technique where an AI model retrieves relevant documents at query time and grounds its answer in them. Being in the retrievable corpus is key to being cited.
LLM (Large Language Model): A model trained on vast text that generates human-like answers — the engine behind ChatGPT, Gemini, Claude, Doubao, and Tongyi Qianwen.
AI Search: Search that returns a synthesized answer with citations instead of a list of links — the shift GEO is built for.
Citation Rate: How often an AI model cites a brand as a source in its answers. Higher citation rate signals stronger authority and trust for that brand.
Entity Linking: Connecting a brand name to a single, unambiguous entity (across languages and aliases) so AI models recognize it consistently. schema.org alternateName helps.
Knowledge Graph: A structured network of entities and their relationships that AI and search engines use to understand and answer about brands, people, and products.
llms.txt: An emerging text file at a site root that gives AI crawlers a curated, machine-readable summary of the site — its services, facts, and key pages.
Structured Data (JSON-LD): Machine-readable schema.org markup (Organization, FAQPage, Article, Product) embedded in a page so engines can parse facts precisely instead of guessing.
Grounding: Tying an AI answer to verifiable sources so it is accurate rather than hallucinated. Well-structured, citable content improves grounding for a brand.
Hallucination: When an AI model states confident but false information about a brand. Authoritative structured data and consistent facts reduce brand hallucinations.
Digital PR: Earning authoritative mentions and citations across the web so AI models encounter a brand repeatedly in trusted sources, raising its recommendation odds.