GEO Glossary: 36 AI Visibility Terms for Local Search

Last updated: July 9, 2026

This GEO glossary defines the core language used to audit, improve, and report local brand visibility in generated answers. Use it to align strategists, account managers, writers, and clients before comparing tools or interpreting AI results.

A practical reference for Generative Engine Optimization, Answer Engine Optimization, prompt tracking, citations, entities, and source analysis.

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How to use this glossary

Link directly to a term’s heading when a report needs a definition. Keep the distinction between observation and interpretation: a mention is not automatically a recommendation, an uncited response is not source proof, and a changed answer is not by itself evidence that one optimization caused the change.

Jump to: Answer Engine Optimization (AEO) · AI answer · AI citation · AI visibility · Answer accuracy · Brand mention · Branded prompt · Canonical entity · Citation gap · Competitor share · Corroboration · Entity · Entity disambiguation · Entity mention · FAQ schema · Generative Engine Optimization (GEO) · Google AI Overview · Grounding · Local AI visibility · Local citation · LLM · Mention rate · Non-branded prompt · Prompt · Prompt family · Prompt tracking · Recommendation rate · Retrieval · Schema markup · Search Engine Optimization (SEO) · Share of voice · Source-backed recommendation · Source gap · Structured data · Uncited mention · White-label reporting

Answer Engine Optimization (AEO)

Related guide: what is AEO.

Definition: The practice of making accurate information easy for an answer system to discover, understand, select, and present.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

AI answer

Definition: A synthesized response generated from model knowledge, retrieved material, tools, or a combination of those inputs.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

AI citation

Definition: A visible reference or link associated with a claim in an AI-generated answer.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

AI visibility

Definition: The degree to which a brand, entity, product, or source appears accurately and favorably across a defined set of AI prompts.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Answer accuracy

Definition: The extent to which an AI response correctly states a business’s services, locations, attributes, and limitations.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Brand mention

Definition: An occurrence of a named brand or recognized variant within a generated answer.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Branded prompt

Definition: A prompt that explicitly includes the company, product, person, or competitor name.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Canonical entity

Definition: The authoritative identity record a business wants systems to resolve across names, URLs, profiles, services, and locations.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Citation gap

Definition: A source or source class that supports competitors or the topic but does not adequately represent the target entity.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Competitor share

Definition: The proportion of tracked prompts in which each competitor appears or is recommended.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Corroboration

Definition: Independent sources agreeing on a material fact, which can increase confidence that the fact is accurate.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Entity

Definition: A distinct person, organization, place, product, or concept that a system can identify and relate to other information.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Entity disambiguation

Definition: The process of separating one entity from similarly named or related entities.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Entity mention

Definition: A reference to a recognized entity, whether or not the exact brand spelling is used.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

FAQ schema

Definition: FAQPage structured data that represents visible questions and answers; it should match the page and does not guarantee a rich result.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Generative Engine Optimization (GEO)

Definition: The practice of improving how clearly and credibly an entity is represented in generated answers.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Google AI Overview

Definition: A generated summary that Google may show for some searches; availability and presentation vary.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Grounding

Definition: Connecting a generated answer to retrieved sources, tools, or supplied context rather than relying only on model parameters.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Local AI visibility

Definition: A local business’s presence and representation in AI answers about services, providers, and decisions in a defined market.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Local citation

Definition: An online reference to a local business, often including its name and other identity or location details.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

LLM

Definition: Large language model: a model trained to process and generate language and often used within conversational or generative systems.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Mention rate

Definition: The percentage of tracked prompt runs in which the target entity is mentioned.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Non-branded prompt

Definition: A discovery prompt that asks about a category, need, or decision without naming the target brand.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Prompt

Definition: The instruction, question, or context submitted to an AI system.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Prompt family

Definition: A controlled group of prompts representing related buyer intents, services, locations, or decision criteria.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Prompt tracking

Related guide: prompt tracking vs keyword tracking.

Definition: Repeatedly running and recording approved prompts to measure entities, context, recommendations, citations, and changes.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Recommendation rate

Definition: The percentage of tracked runs in which the target is presented as a suitable choice, not merely named.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Retrieval

Definition: The process of finding external or indexed information to help answer a query.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Schema markup

Definition: Machine-readable structured data describing visible page content and entities using a vocabulary such as Schema.org.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Search Engine Optimization (SEO)

Definition: The practice of improving discoverability, usefulness, and performance in organic search results.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Share of voice

Definition: A comparative measure of how much visibility one entity receives versus defined competitors across a query or prompt set.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Source-backed recommendation

Definition: A recommendation accompanied by a visible citation or otherwise traceable supporting source.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Source gap

Definition: Missing or weak first- or third-party evidence relative to the information needed for an answer or competitor advantage.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Structured data

Definition: Standardized machine-readable information added to a page to describe its visible content and relationships.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

Uncited mention

Definition: A brand reference in an answer without a visible supporting source; it can be useful but is harder to diagnose and may be less stable.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

White-label reporting

Definition: Reports presented under an agency’s brand for delivery to its clients.

Why it matters: This term helps teams separate observable answer behavior from assumptions, choose an appropriate metric, and explain findings consistently to clients.

A simple measurement hierarchy

LevelQuestionExample metric
DiscoveryDoes the entity appear?Mention rate
FitIs it presented as relevant?Recommendation rate
TrustIs support visible?Source-backed recommendation rate
AccuracyAre facts correct?Answer accuracy score
CompetitionWho wins instead?Competitor share
DiagnosisWhat evidence shapes the answer?Source coverage and gaps

Applying the terms in a local audit

Define the entity and market first. Approve prompt families, record complete answers, identify mentions and recommendations, capture citations, assess accuracy, compare competitors, and turn source gaps into a prioritized action queue. Retest after meaningful work and explain uncertainty rather than presenting an AI response as a fixed rank.

The GEO audit template converts these definitions into a repeatable client workflow.

Frequently Asked Questions

What does GEO mean in digital marketing?

GEO means Generative Engine Optimization, the practice of improving how clearly and credibly an entity appears in AI-generated answers.

Are GEO and AEO different?

They have different emphases but overlap operationally. AEO focuses on being selected as an answer; GEO focuses on representation across generated responses.

What is the difference between a mention and a recommendation?

A mention only names the entity. A recommendation presents the entity as a suitable choice for the user’s need.

What is a source-backed recommendation?

It is a recommendation accompanied by a visible citation or traceable supporting source, making the evidence easier to evaluate.

What should local agencies track?

Track mentions, recommendations, citations, answer accuracy, competitor share, source coverage, and completed remediation across approved prompt families.

New to the discipline? Start with what is GEO for the complete operating model. Agencies turning these definitions into a commercial scope can then use the GEO retainer pricing guide.