A
- AEO (AI Engine Optimization)
- The practice of optimizing content and digital presence to improve visibility and recommendations within AI-powered search engines and assistants. Think SEO, but for ChatGPT, Gemini, and Perplexity.
- AI Answer Engine
- A search interface that generates synthesized answers using large language models instead of returning a list of links. Examples: ChatGPT, Perplexity, Google AI Overviews.
- AI Crawlability
- How accessible your content is to AI systems that index and retrieve information for generating answers. Not the same as Google crawlability.
- Agentic Commerce
- The emerging paradigm where AI agents make purchasing decisions or recommendations on behalf of users, bypassing traditional search and discovery.
B
- Brand Mention Tracking
- Monitoring when and how AI engines reference a specific brand in their generated responses.
C
- Citation
- When an AI engine references a specific source (URL, brand, or content) in its generated answer. The AI equivalent of a backlink.
- Content Authority Signal
- Indicators that help AI engines determine which sources to trust and cite, including domain expertise, content depth, and third-party validation.
D
- Discovery Layer
- The interface through which users find information — shifting from traditional search (Google) to AI-powered answers (ChatGPT, Gemini, Perplexity).
E
- Entity Recognition
- How AI engines identify and categorize brands, products, and topics within content. Strong entity recognition increases citation likelihood.
G
- Ghost Routes
- Pages that rank well in Google Search Console but have zero presence in AI search results. A term coined by visibl to describe the indexing gap between traditional search crawlers and LLM retrieval systems.
- Grounding
- The process by which AI engines verify generated answers against source material. Well-grounded responses include citations; poorly grounded ones hallucinate.
H
- Hallucination
- When an AI engine generates information that isn't supported by its training data or retrieved sources. Can result in incorrect brand mentions or fabricated claims.
I
- Indexing Gap
- The difference between what traditional search engines index and what AI engines can access and cite. Many pages indexed by Google are invisible to AI retrieval systems.
K
- Knowledge Graph
- A structured database of entities and their relationships that AI engines use to understand and connect concepts. Being represented in knowledge graphs increases AI visibility.
L
- LLM (Large Language Model)
- The AI models powering answer engines — GPT-4, Gemini, Claude, etc. Understanding how LLMs process and prioritize information is key to AI visibility.
M
- Model Training Data
- The corpus of text used to train an LLM. Content in training data may influence an AI engine's baseline knowledge about a brand, separate from real-time retrieval.
P
- Prompt Sensitivity
- How variations in user queries affect which brands and sources an AI engine cites. The same question phrased differently can produce completely different brand recommendations.
R
- RAG (Retrieval-Augmented Generation)
- A technique where AI engines retrieve relevant documents in real-time to augment their responses. RAG is why fresh, well-structured content matters for AI visibility.
- Recommendation Frequency
- How often an AI engine suggests a particular brand or product across relevant queries. A key metric in visibl's monitoring dashboard.
S
- Semantic Structure
- How content is organized to help AI engines understand topics, relationships, and hierarchy. Clean semantic structure improves AI crawlability and citation rates.
- Source Attribution
- When an AI engine credits a specific website or content piece as the basis for its answer. Source attribution is the currency of AI visibility.
T
- Topical Authority
- The degree to which AI engines recognize a domain as an expert source on a specific subject. Built through comprehensive, interlinked content clusters.
- Training Cutoff
- The date after which an LLM has no inherent knowledge. Content published after the cutoff relies entirely on RAG for AI visibility.
V
- Visibility Score
- A composite metric measuring a brand's overall presence and positioning across AI engines. Combines citation frequency, recommendation sentiment, and competitive share.
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