AI Marketing & GEO Glossary

AI search refers to search experiences powered by large language models that generate direct, synthesised answers to user queries rather than returning a ranked list of links.

AI-powered search is a search experience in which large language models generate direct, conversational answers to queries rather than returning a ranked list of links — integrating AI synthesis with traditional search infrastructure.

Answer Engine Optimization (AEO) is the discipline of structuring content, entities, and authority signals so that AI-powered answer engines — including ChatGPT, Perplexity, and Google AI Overviews — surface your brand in direct responses to user queries.

A category query is a question a user poses to an AI answer engine seeking to discover, compare, or evaluate options within a product or service category — such as 'what's the best CRM for small businesses?' or 'which project management tools support Gantt charts?'

Generative Engine Optimization (GEO) is the discipline of structuring content, entities, and authority signals so that large language model-powered answer engines surface and cite your brand in their responses.

Generative search is a search paradigm in which large language models generate synthesised, conversational responses to user queries — combining document retrieval with AI text generation to produce direct answers rather than ranked link lists.

An intent query is a user prompt submitted to an AI answer engine that reveals the underlying goal driving the question — whether that is discovering options, comparing alternatives, completing a task, or solving a problem.

A knowledge graph is a structured database that represents real-world entities — people, organisations, products, places — as nodes, and the relationships between them as edges, enabling AI and search systems to reason about the world with greater accuracy.

LLM SEO is the practice of optimising websites, content, and entity signals so that large language models (LLMs) retrieve, cite, and recommend your brand when generating answers.

Position zero in AI search refers to the most prominent brand recommendation or citation in an AI-generated answer — the first or leading brand mention that a user encounters when asking an AI system for a recommendation in a category.

Zero-click search occurs when a user receives a complete, satisfactory answer from a search or AI engine directly in the results interface, without needing to click through to any external website.

AI brand monitoring is the systematic practice of tracking how, how often, and with what sentiment a brand is mentioned in responses generated by AI answer engines such as ChatGPT, Perplexity, and Google AI Overviews.

AI brand visibility is a measure of how frequently and how prominently a brand is mentioned, cited, or recommended in responses generated by AI answer engines such as ChatGPT, Perplexity, and Google AI Overviews.

AI marketing encompasses two related disciplines: using artificial intelligence tools to improve marketing operations and performance, and optimising brand presence in AI-powered channels where buyers increasingly discover and evaluate products.

AI search visibility is the aggregate measure of a brand's presence across AI-powered search and answer platforms, encompassing mention frequency, citation prominence, and sentiment across the full landscape of AI search products.

An AI SEO audit is a structured assessment of a brand's current visibility in AI-generated answers, evaluating mention rates, sentiment, competitive positioning, and the technical and content factors that influence AI citation likelihood.

Brand entity optimization is the practice of ensuring your brand is accurately, consistently, and comprehensively represented as a distinct entity across the web — including directories, Wikipedia, structured data, and authoritative publications — to improve AI system recognition and citation.

Brand prompt testing is the systematic practice of submitting a defined set of queries to AI systems and recording brand mentions, positions, and sentiment in the responses — providing a measurable basis for AI brand visibility assessment.

LLM brand tracking is the ongoing practice of monitoring brand mentions, sentiment, and competitive positioning across responses generated by large language models, providing systematic visibility into how AI systems represent your brand over time.

LLM visibility is the degree to which a brand, product, or entity is represented — cited, recommended, or described — in responses generated by large language models.

AI content strategy is the discipline of planning, creating, and structuring content with the explicit goal of improving a brand's visibility and citation frequency in AI-generated answers across LLM and AI search platforms.

Citation optimization is the practice of structuring web content so that AI retrieval systems are more likely to select it as a source to cite when generating answers to user queries.

Content authority is the degree to which a brand's published content is recognised as expert, reliable, and trustworthy by search engines and AI systems — influencing its likelihood of being retrieved and cited in AI-generated answers.

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness — Google's framework for evaluating the quality and credibility of web content, increasingly applied to AI content selection as well.

RAG optimization is the practice of structuring web content so that retrieval-augmented generation AI systems are more likely to retrieve, select, and cite it when generating responses to relevant user queries.

Structured data for AI refers to the use of schema.org markup and JSON-LD to provide AI retrieval systems with explicit, machine-readable signals about the content, entities, and relationships on a web page.

Topical authority is the degree to which a website or brand is recognised as a comprehensive, expert source on a specific subject area — measured by breadth and depth of coverage, internal linking, and external citations from other authoritative sources in the same domain.

Training data optimization is the practice of publishing and distributing content that is likely to be included in LLM training datasets, thereby improving a brand's representation in the base knowledge of large language models.

AI search ranking is a qualitative measure of how prominently a brand appears within an AI-generated response — distinguishing between being the primary recommendation, a named option in a list, a secondary mention, or absent entirely.

AI share of voice (AI SOV) is your brand's proportion of all brand citations in AI-generated responses to a defined set of category queries, expressed as a percentage of total citations across all brands mentioned.

An AI visibility score is a composite metric that aggregates multiple AI brand monitoring signals — mention rate, share of voice, sentiment, and position — into a single, normalised index that can be tracked over time and compared against competitors.

Brand mention rate is the percentage of AI-generated responses to a defined set of category queries that include at least one mention of a specific brand.

Competitor AI benchmarking is the process of measuring and comparing the AI-generated answer visibility of your brand against key competitors, across a defined set of category queries and AI platforms.

Mention velocity is the rate of change in a brand's AI mention frequency over a defined time period — measuring whether AI-generated citations of a brand are accelerating, stable, or declining.

Sentiment scoring in AI brand monitoring is the process of analysing AI-generated responses that mention your brand to classify the tone of those mentions as positive, neutral, or negative.

An AI answer engine is a system that uses large language models to generate a direct, synthesised response to a user's query, rather than returning a ranked list of web links.

ChatGPT optimization is the practice of improving the frequency and prominence with which a brand appears in responses generated by ChatGPT and other OpenAI products.

Google AI Overviews SEO is the practice of optimising web content to be selected and cited in Google's AI-generated summary answers, which appear above traditional organic search results.

Perplexity optimization is the practice of improving the frequency and prominence with which a brand appears in Perplexity AI's search responses, which use real-time web retrieval combined with LLM synthesis.

Retrieval-Augmented Generation (RAG) is an AI architecture in which a language model retrieves relevant documents from an external source (typically a search index) and uses them as context before generating a response, combining real-time information retrieval with language model synthesis.