Generative Engine Optimization (GEO) Glossary

A practical A–Z of terms for optimizing visibility in AI-generated answers and AI-driven search.

Ahrefs: SEO platform; its brand-monitoring tools help track mentions in AI answers.
AI Overviews: Google’s AI-generated answer panels that synthesize content at the top of results.
AI-Driven Search Engines: Search experiences that return conversational, generated answers (e.g., ChatGPT, Perplexity, Claude, Gemini).
Answer Engine Optimization (AEO): Optimizing content to be selected as the answer, closely related to GEO.
Authoritative Content: Material that demonstrates clear expertise and trust—more likely to be cited by AI systems.

Answer Engine: A system that generates direct answers to queries instead of a list of links.
Attribution: How a generative engine cites or links to the sources used to compose its response.
Anchor Citations: Inline or end-of-answer citations that help models (and users) verify claims.

Bing: Microsoft search with Copilot-powered generative answers.
BingChat / Copilot (legacy name): Microsoft’s conversational search that evolved into Copilot.
Brand Mentions: References to a brand inside AI answers—useful signal of awareness.

Brand Radar: Tools that track when/where a brand appears in AI-generated content.
Byline Signals: Explicit author credentials and bios that bolster model trust and selection.

ChatGPT: OpenAI’s conversational model and major GEO surface.
Citation Optimization: Structuring content with clear, credible citations to increase selection.
Claude: Anthropic’s assistant; another key generative surface.
Co-Citations: Your site mentioned alongside authoritative sources—boosts topical signals.

Co-Occurrences: Related entities/terms appearing together; helps models understand context.
Conversational Search: Natural-language, multi-turn queries common in AI search experiences.
Canonical Source: The definitive page for an entity or topic you want models to “remember.”

Domain-Specific Optimization: Tactics tailored to your industry’s language and evidence standards.
Deduplication: Ensuring one high-confidence version of content exists to avoid model confusion.

Data Provenance: Clear traceability of facts and sources—improves trust and inclusion.

E-E-A-T: Experience, Expertise, Authoritativeness, Trustworthiness—still core signals.
Entity Recognition: How models identify people, places, orgs, and concepts in content.
Embeddings: Numeric representations of text that power semantic search and RAG.

Entity Home: The page that definitively describes an entity (you, your product, your brand).
Evals (Evaluation): Systematic tests to measure answer quality, grounding, and citation rates.

Featured Snippets: Direct answers in classic SERPs—often training/grounding inputs for AI.
First-Party Data: Your owned data (studies, surveys, docs) that models can cite.

Freshness: Recency signals that influence whether your content is selected today.

Gemini: Google’s model family used across products.
Generative Engine: Systems that create answers by synthesizing multiple sources.
GEO (Generative Engine Optimization): Improving the chance your content is selected, cited, and remembered by AI.

GEO-BENCH: Research benchmark for evaluating GEO strategies.
Grounding: Connecting an answer to verifiable sources to reduce hallucinations.

HubSpot: Marketing platform; includes tools that assess AI search visibility.
Hybrid Systems: Models that blend training data with live web results for answers.

Hallucination: When a model states an incorrect or ungrounded claim.

Informational Intent: Queries seeking knowledge; common trigger for AI answers.
Investigational Intent: Comparisons and research-phase queries.

Indexing (Model Memory): Ensuring models ingest and can recall your canonical content.

JSON-LD: Structured data format for schema markup that clarifies entities.

Judgment Tokens: Heuristic signals/models use to weigh confidence in citing a source.

Knowledge Graph: Entity-relationship data that helps models connect topics and brands.

Key Passage: A concise, quotable block that increases the chance of being cited.

Large Language Models (LLMs): AI systems trained on vast corpora that generate human-like text.
LLM Traffic: Visits referred from AI assistants and generative search experiences.

Linkable Assets: Original research, tools, and visuals that attract citations.

Microsoft: Owner of Bing and Copilot; major AI search surface.
Model Memory: Whether models “remember” your brand/content for relevant queries.

MCP (Model Context Protocol): A standard for connecting models to tools/data via servers.

Natural Language Processing (NLP): Tech that enables machines to understand human language.
Navigational Intent: Queries where users seek a specific site or page.

Node Authority: Perceived credibility of an entity within a knowledge graph.

OpenAI: Creator of ChatGPT and GPT models.
On-Page Evidence: Quotes, citations, stats, and media that strengthen answer selection.

Original Research: Unique data/models prefer to cite; boosts authority and links.

Perplexity: An AI-powered answer engine and discovery tool.
Pipedream: Integration platform—often used to connect AI assistants via MCP.
Prompt Engineering: Crafting inputs that elicit useful answers from models.

Prompt Injection: Malicious instructions that try to override a model’s safeguards.
Prospect Answers: Drafts you publish that mirror how AI would answer key questions.

Query Intent: Understanding the job-to-be-done behind a search.
Quotation Addition: Adding expert quotes to boost credibility and selection.

Query Embedding: Vectorizing a query so models can find semantically similar passages.

Response Generation: How models synthesize multi-source answers.
RAG (Retrieval-Augmented Generation): Technique where a model retrieves relevant sources to ground its answer.

Relevance Signals: Topical coverage, entities, citations, freshness, and depth.

Search Everywhere: Discovery happens across assistants, chat, apps—not just classic SERPs.
SGE (Search Generative Experience): Google’s AI-generated answer experience in search.
Semrush: Marketing suite; publishes AI search research and trends.

Source Attribution: Linking back to origin pages used by the model.
Statistics Addition: Including concrete data to increase selection and citations.
Schema Markup: Structured data (e.g., JSON-LD) that clarifies entities and relationships.

Technical Terms: Industry vocabulary that signals expertise.
Training Data: Information used to train models; influences what’s “remembered.”
Transactional Intent: Queries with purchase/action intent.

Topical Authority: Depth and breadth on a subject; increases your chance of being cited.
Trust Signals: Author bios, references, original research, and transparent claims.

Unaided Awareness: Models mention your brand without prompting—strong signal of memory.

Use-Case Pages: Pages framed around problems and outcomes—often selected by AI.

Visibility Metrics: Tracking inclusion/citations in AI answers (beyond classic rankings).
Voice Search: Spoken queries to assistants and devices.

Vector Database: Stores embeddings for semantic retrieval used by RAG pipelines.

Writesonic: AI writing platform with GEO-focused tooling.

Web Grounding: Explicitly linking to current sources so models can verify claims.
White-Paper Evidence: Downloadable, citable research that earns links and model trust.

XML Sitemaps: Help discovery and crawling; ensure your canonical pages are indexable.

X-Ref (Cross-Reference): Internal links that connect related entities/topics.

YMYL: “Your Money or Your Life” topics that require stronger evidence and trust.

YAML: Human-readable data format sometimes used in tooling/configs for AI apps.

Zero-Click Searches: Users get answers without clicking; both an opportunity and a challenge.

Zero-Shot: Model performs a task without task-specific examples—relevant for prompts.