AEO Glossary — AI & Search Engine Terms Defined
Every term used in AI visibility optimization, defined with practical context for why it matters to your business.
The Trinity Framework
The three pillars that define your AI visibility score.
AEO (Answer Engine Optimization)
Optimizing web content so AI answer engines (ChatGPT, Gemini, Perplexity, Claude) can extract, understand, and cite it. AEO carries 40% of the Trinity Formula weight because content citability has the strongest correlation with actual AI citation rates.
Key techniques: BLUF formatting, passage citability optimization, semantic branching, and structured answer patterns.
GEO (Generative Engine Optimization)
The technical foundation of AI visibility, weighted at 25%. Encompasses schema.org markup, crawl accessibility, structured data architecture, and signals that help AI engines discover and parse your content.
GEO is analogous to technical SEO but with AI-specific priorities. JS-rendered content that traditional crawlers handle may be invisible to AI training pipelines.
EVS (Entity Verification Score)
Measures how well AI engines can independently verify your business entity across the web. Weighted at 35%. Includes NAP consistency, third-party citations, reviews, credentials, and cross-platform entity coherence.
Often the hardest dimension to improve because it depends on signals outside your own website.
Trinity Formula
The composite scoring equation: GEO + AEO + EVS (proprietary weights). Developed through analysis of 500+ AI visibility assessments and validated against actual citation rates across four AI engines. Recalibrated quarterly.
Scoring & Metrics
K-Score (K1 through K7)
Seven individual scoring components on a 0-100 scale that roll up into the Trinity composite. K1: citation presence, K2: content structure, K3: entity consistency, K4: schema/technical, K5: passage citability, K6: authority signals, K7: competitive positioning.
Each K-Score carries a data provenance label indicating LIVE, CACHED, or ESTIMATED sourcing.
RevScore IQ
RevHome.ai's proprietary AI visibility scoring platform. Runs 100+ LLM probes across four AI engines, audits technical infrastructure, and measures entity verification signals. Produces a composite score from 0-100.
Available in three tiers: Express, Pro, and ScoreMax.
LLM Probe
A structured query sent to an AI engine to test citation behavior. Each RevScore IQ assessment runs 100+ probes across ChatGPT, Gemini, Perplexity, and Claude using varied query formulations to test citation consistency.
Citation Velocity
The rate at which a business accumulates new AI citations over time. Measures momentum rather than absolute count. A leading indicator tracked as part of the EVS dimension.
Writing & Structure Terms
BLUF (Bottom Line Up Front)
Content structuring technique where the key answer is placed in the opening sentence. Critical for AEO because AI engines prioritize content that delivers extractable answers without parsing lengthy introductions.
Passage Citability
How many discrete, self-contained, factually verifiable passages a piece of content contains that an AI engine could cite independently. Top-performing content averages 11 citable passages per 1,000 words. The median site scores below 3.
Fact Density
The ratio of verifiable factual claims to total word count. Higher fact density correlates with higher passage citability. Every paragraph should contain at least one specific, checkable claim.
RAG (Retrieval-Augmented Generation)
AI architecture where the model retrieves relevant documents before generating responses. RAG inversion is a strategy where you structure content specifically to be the retrieved document for target queries.
Semantic Branching
Content architecture where a central pillar branches into semantically related subtopics, each with a dedicated page. Creates a tree structure AI engines traverse to assess topical authority.
Verification & Provenance
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)
Google's quality framework, now equally relevant for AI source selection. For AI visibility, E-E-A-T manifests in measurable signals: author credentials in schema, third-party citations, entity consistency, and verifiable claims.
NAP Consistency
Uniformity of Name, Address, Phone across all platforms. AI engines cross-reference entity information to build citation confidence. Even minor inconsistencies ("Street" vs "St.") reduce EVS scores.
Tier Labels (Data Provenance)
Every RevScore IQ data point carries a provenance label: LIVE (real-time API data), CACHED (prior run with timestamp), ESTIMATE (derived value with formula), or UNAVAILABLE (API failed, no substitution). See our AI Transparency page.
LLM (Large Language Model)
The AI technology powering modern answer engines. RevHome.ai tests across multiple LLMs simultaneously because citation behavior varies significantly between models.
YMYL (Your Money or Your Life)
Content classification for topics impacting health, finances, or safety. YMYL businesses face stricter AI source verification, making EVS signals even more critical.
Query Formulation Variance
The range of ways users phrase questions to AI engines. High citation variance across phrasings indicates narrowly optimized content rather than comprehensive authority.
Ready to Measure Your AI Visibility?
Now that you know the terminology, see where your business stands. Get your Trinity score across all seven K-Score dimensions.