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 AI citation rate before and after Adaptive RAG implementation. Calculated by testing the same prompt set across all major AI models and measuring the percentage increase in brand mentions. Example: Filmfolk achieved +81pp citation delta (0% → 81%).

The percentage of tracked prompts where your brand is cited by AI systems. Measured across a consistent prompt set (typically 40-80 prompts) and tracked per model (ChatGPT, Claude, Perplexity, Gemini). A 70%+ citation rate indicates strong AI visibility.

A measure of how clearly and consistently an entity (brand, person, product) is represented across weighted sources in AI retrieval systems. High entity confidence means AI systems can confidently identify who you are, what you do, and why you're relevant. Low confidence results in zero citations.

The degree to which your brand-role-category relationship is consistent across all sources. Strong entity clarity means AI systems understand exactly what you do and can retrieve you in the right context. Weak clarity causes AI to skip your brand even if you have strong content.

The gradual reduction in retrieval priority as signals age. AI models weight newer information more heavily than older data. Without continuous freshness management, even strong entity signals decay over time as models update with newer information.

See AEO (Answer Engine Optimization). Both terms refer to optimizing for AI-generated answers rather than traditional search rankings.

An emerging standard file format (similar to robots.txt) that provides structured information to AI systems. Contains brand facts, entity relationships, and key URLs in a machine-readable format. Recommended for all brands optimizing for AI visibility.

The number of buyer prompts where your brand appears compared to the total tracked prompt set. High prompt coverage means you're cited across diverse query types (recommendations, comparisons, how-to, best-of). Low coverage means you only appear in narrow contexts.

Specific, verifiable facts that AI systems can cite with confidence. Examples: "81% citation rate in 90 days," "Filmfolk: 0% to 81%," "Five-step Adaptive RAG framework." Proof points must be consistent across all sources to strengthen entity confidence.

The knowledge layer AI systems query when generating answers. Consists of training data, real-time web retrieval, and structured sources. Brands must be represented clearly in this layer to be cited. Traditional SEO optimizes for Google's index; AEO optimizes for the retrieval layer.

The weighted ranking of source types in AI retrieval. From lowest to highest trust: Owned sources (website, blog) → Earned sources (press, reviews) → Structured sources (schema, llms.txt) → Expert sources (academic, industry reports). Most brands over-index on owned content.

The process of assigning trust weights to sources based on authority, freshness, and retrieval priority. Part of the Adaptive RAG WEIGHT step. Ensures higher-trust sources carry more influence in AI retrieval decisions.

Machine-readable formats that help AI systems understand entity relationships. Includes JSON-LD schema markup, llms.txt files, and knowledge graph entries. Structured data is high-priority in AI retrieval — more important than unstructured content volume.

The continuous process of strengthening brand-role-category signals across all touchpoints. Part of the Adaptive RAG REINFORCE step. Includes updating freshness signals, monitoring competitor positioning, and adapting to model behavior changes.

The first step in Adaptive RAG (MAP). Testing real buyer prompts across ChatGPT, Claude, Perplexity, and Gemini to capture baseline citation data. Identifies where your brand is missing, who's winning instead, and what the signal gaps are.

The alignment of how your brand, its role, and its category are described across all sources. Example: "RAG Signal (brand) is an AI visibility engineering firm (role) in the GEO/AEO category (category)." Inconsistency reduces entity confidence.

The degree to which your brand is mentioned, cited, or recommended in AI-generated answers. Measured by citation rate, prompt coverage, and citation accuracy. Strong AI visibility means buyers see your brand when researching vendors through AI systems.