AI visibility glossary

A proprietary framework that continuously adjusts which sources enter the AI retrieval layer, how they are weighted, how fresh they are, and how consistently they reinforce brand entity signals. Unlike static knowledge injection, Adaptive RAG adapts to model behavior, competitor pressure, and prompt patterns.

The practice of optimizing brand signals to be cited in AI-generated answers. Also called Generative Engine Optimization (GEO). AEO focuses on entity clarity, source trust, and structured data — distinct from traditional SEO which targets page rankings.

A curated, weighted knowledge layer that maps entity relationships, source trust hierarchy, and factual proof points into formats AI retrieval systems can read and cite. Brand Memory is RAG Signal's proprietary methodology for ensuring brands are retrievable and recognizable in AI systems.

The measurable change in how often a brand is cited in AI-generated answers over a defined period. Citation delta is the primary KPI in RAG Signal engagements — tracking movement from baseline to target citation rate across ChatGPT, Claude, Perplexity, and Gemini.

The percentage of relevant AI-generated answers that include a specific brand as a cited source or named entity. A brand with a 40% citation rate appears in 40 out of 100 relevant AI answers.

An internal signal used by AI retrieval systems to assess how reliably a named entity (brand, person, concept) can be identified, described, and cited. Higher entity confidence correlates with more consistent citation behavior.

The process of strengthening how AI systems recognize and describe a brand entity — through structured data, consistent naming, factual proof points, and cross-source corroboration. A core component of Brand Memory construction.

The discipline of optimizing content and brand signals for retrieval by AI answer engines — ChatGPT, Claude, Perplexity, Gemini — rather than traditional search engines. GEO focuses on entity clarity, source trust, and structured retrieval signals.

The probability that an AI system will generate inaccurate or fabricated information about a brand when answering relevant queries. High hallucination risk indicates weak entity grounding — a Brand Memory problem.

The process by which large language models select, weight, and incorporate information from their training data or retrieval-augmented sources when generating answers. Understanding LLM retrieval behavior is the foundation of RAG Signal's methodology.

The percentage of relevant buyer prompts — questions real users ask AI systems — for which a brand appears in the generated answer. Prompt coverage maps the actual surface area of AI visibility.

An AI architecture that supplements a language model's parametric knowledge with real-time or curated document retrieval. RAG systems retrieve relevant sources before generating an answer, making source quality and entity clarity critical for brand visibility.

A brand's proportional presence in AI-generated answers relative to competitors, measured across a defined set of prompts and AI models. AI share of voice is distinct from traditional search share of voice.

A weighting system that determines how much influence a given source has on AI retrieval behavior. Sources with higher trust scores — authoritative domains, consistent entity signals, structured data — are more likely to be cited.

A composite metric tracking a brand's overall presence in AI-generated answers across models, prompt categories, and time periods. RAG Signal's visibility score normalizes citation rate, prompt coverage, and share of voice into a single benchmark.