Reference
GEO Glossary
33 key terms in Generative Engine Optimization — from RAG architecture to citation mechanisms. Based on the Princeton GEO research (KDD 2024) and industry data.
Core
- GEO (Generative Engine Optimization)
- The practice of optimizing content to be cited and referenced by AI search engines. Coined by Aggarwal et al. (Princeton/IIT Delhi/Georgia Tech, KDD 2024).
- Generative Engine
- A search system that combines traditional retrieval with large language models to generate synthesized answers with inline citations. Examples: ChatGPT Search, Perplexity, Google AI Overviews.
- RAG (Retrieval-Augmented Generation)
- A 4-stage architecture used by AI search: query understanding → retrieval → re-ranking → generation + citation. The LLM generates answers from retrieved passages rather than its training data alone.
- AEO (Answer Engine Optimization)
- A broader term for optimizing content for answer-based search systems. GEO is a subset focused on generative engines specifically.
- Citation
- An inline reference in an AI-generated answer, linking back to the source passage. The primary currency of GEO visibility.
- Mention Rate
- The percentage of representative prompts in which a brand appears in AI-generated answers. The core GEO measurement metric, replacing traditional "rankings."
- Position-Adjusted Word Count
- A GEO evaluation metric from the Princeton study that measures cited content length weighted by display position. Higher position = higher score.
Platforms
- OAI-SearchBot
- OpenAI's web crawler for ChatGPT Search indexing. Must be explicitly allowed in robots.txt for GEO visibility on ChatGPT.
- GPTBot
- OpenAI's crawler for model training data. Distinct from OAI-SearchBot (which is for search indexing).
- PerplexityBot
- Perplexity AI's web crawler. Perplexity uses a strong citation model with 5-15 numbered references per answer.
- Claude-SearchBot
- Anthropic's crawler for Claude's web search feature. Claude also uses ClaudeBot (training) and Claude-User (user-initiated browsing).
- Google-Extended
- Google's crawler for AI training data (used by Gemini). Distinct from Googlebot which handles search indexing.
- Google AI Overviews (AIO)
- Google's AI-generated answer summaries shown above traditional search results. Covers ~16% of queries as of 2025, up from 6.49% earlier.
- Applebot-Extended
- Apple's crawler for AI features in Apple Intelligence. Should be allowed in robots.txt for GEO.
Strategies
- Expert Quotation
- A GEO strategy that adds direct quotes from named experts. The Princeton study found this boosts AI visibility by +41%, the highest single-strategy lift.
- Statistics Addition
- Replacing vague descriptions with specific, sourced statistics. +33% visibility lift. Especially effective for law, policy, and business content.
- Fluency Optimization
- Improving text readability and logical flow. +29% lift. Best combined with statistics for an additional +5.5% bonus.
- Cite Sources
- Adding authoritative source citations to key claims. +28% lift. Most effective for factual and declarative queries.
- Keyword Stuffing
- A traditional SEO tactic of repeating keywords. In GEO, this REDUCES visibility by -8%. The only harmful strategy tested.
- Factual Density
- The concentration of verifiable facts (numbers, dates, names) in content. A key factor in AI citation decisions.
- Co-citation
- When a brand is mentioned alongside competitors in third-party content. AI systems use this to build competitive entity associations.
- Co-occurrence
- When a brand frequently appears in the same topic context. AI systems use this to associate brands with subject areas.
Technical
- GEO-bench
- A benchmark of 10,000 real search queries across 9 datasets, built by the Princeton team to evaluate GEO strategies. Covers informational, transactional, and navigational queries.
- Schema.org
- Structured data vocabulary that helps AI systems understand content. Key GEO types: Organization, Article, FAQPage, HowTo.
- FAQPage Schema
- Structured data marking Q&A content. Makes it easier for AI to extract and cite answers.
- llms.txt
- A proposed standard (by Jeremy Howard, Answer.AI) for providing LLM-friendly content maps. SERanking's study of 300,000 domains found no correlation with AI citations.
- Cross-Encoder
- A re-ranking model that scores candidate passages by relevance. Used in stage 3 of the RAG pipeline. Higher-quality, better-structured content wins here.
- BM25
- A keyword-based retrieval algorithm used alongside vector search in stage 2 of RAG. Combines with embedding similarity to find candidates.
- SSR (Server-Side Rendering)
- Rendering HTML on the server rather than client-side. Critical for GEO because AI crawlers often cannot execute JavaScript.
- E-E-A-T
- Experience, Expertise, Authority, Trust — Google's quality framework. Also influences AI citation decisions as a credibility signal.
Measurement
- Share of Voice
- A GEO metric measuring how often a brand is mentioned vs. competitors across a set of AI search prompts.
- Citation Frequency
- The number of times a brand is cited as a source in AI-generated answers across a representative prompt set.
- Sentiment Analysis
- Tracking whether AI describes a brand positively, neutrally, or negatively in its answers.
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