AI SEO & GEO Glossary:
Key Terms for Modern Search Marketing

Tailoring your content so AI assistants incorporate it reliably and favourably into their generated answers.

Techniques for tailoring content so large language models recognise and cite it.

Occurrences when artificial intelligence platforms mention, source, or suggest particular brands within their automated responses to user inquiries.

How AI systems attribute information to the original sources they use.

A structured grouping of related articles around a central theme to build topical authority.

A metric indicating how frequently your brand is cited in AI-generated answers.

Techniques for optimizing content to appear prominently and accurately in AI-generated responses across different platforms.

Demonstrated expertise and credibility on a specific subject or niche.

Monitoring and analysing how your content performs across AI-driven search platforms.

Digital marketing strategy focused on ensuring AI models cite your content in their generated responses.

Developing content with AI systems in mind from the very beginning.

The chance that an AI system will cite or reference your content when generating an answer.

An alternative term for GEO that highlights optimisation for AI systems that generate answers rather than retrieve links

The perceived credibility of a particular article or author on a specific topic.

The process how AI prioritize and order content when generating responses, determining which sources get cited or referenced.

A holistic strategy for optimizing visibility across every search and discovery channel, from traditional engines to AI assistants and social platforms.

Web applications that behave like native apps, offering offline access and enhanced engagement through the browser.

Creating content with the goal of shaping how future AI models learn about your brand or topic.

Tactics to improve the likelihood of your brand being cited in ChatGPT conversations.

A metric indicating the percentage of AI responses that cite your brand or content.

The time users spend on a page before returning to the search results, reflecting engagement and content usefulness.

The time it takes for a web page to load, a critical factor for user satisfaction, SEO and AI evaluation.

The ratio of users who click on your result compared to those who view it, reflecting attractiveness and relevance.

Accelerated Mobile Pages (AMP) provide a stripped‑down HTML framework for delivering lightning‑fast mobile experiences.

Preferred URL versions used to consolidate ranking signals and avoid duplicate content issues.

The optimization of video content for better discovery, ranking and engagement on search engines and platforms like YouTube.

Google’s practice of primarily using the mobile version of a site’s content for indexing and ranking.

All aspects of how users interact with and perceive a website or application—usability, design, performance and satisfaction.

Structured files listing a website’s important URLs, helping search engines and AI crawlers discover and index content efficiently.

The page displayed in response to a query, featuring organic listings, ads and rich features like snippets and AI overviews.

The percentage of visitors who leave after viewing only one page, signalling engagement or misalignment with intent.

A branch of SEO focusing on optimizing visibility in local searches and map listings.

Structured data vocabulary used to annotate content for search engines and AI models, enabling rich results and better understanding.

A Google ranking factor encompassing Core Web Vitals, mobile‑friendliness, safe browsing, HTTPS and intrusive interstitial guidelines.

Search queries where users obtain answers directly from the results page without visiting any website.

Connecting pages within a site using hyperlinks to distribute authority, aid navigation and improve crawlability.

A search method where users speak queries to assistants like Alexa, Google Assistant or Siri, requiring conversational and local optimization.

A classification for content that could impact a user’s financial stability, health, safety or overall well‑being, subject to strict quality standards.

An information box appearing in search results with structured data about entities like people, organizations or places.

The practice of tailoring content to meet the needs of voice‑activated queries, focusing on conversational language and local relevance.

Google’s framework evaluating the Expertise, Authoritativeness and Trustworthiness of content and creators.

The processes by which search and AI bots discover, analyze and store web content for retrieval.

A network of interconnected entities and relationships used by search engines and AI systems to understand context and facts.

The underlying goal behind a search query, such as seeking information, navigating to a site, making a purchase or researching options.

Concise answer boxes displayed at the top of search results, providing quick information without requiring clicks.

Longer, more specific phrases (often three or more words) that capture targeted search intent and conversational queries.

A holistic discipline focused on enhancing visibility, relevance and ranking across search engines and AI platforms.

A free tool providing insights into your site’s visibility, indexing, performance and technical health.

Special elements on search results pages—beyond blue links—that provide immediate answers or enhanced information.

Hyperlinks from other websites that act as endorsements, signalling authority and trust to search engines and AI systems.

The practice of reducing file sizes and adding context so images load quickly and are properly understood by search engines and AI models.

A comparative metric estimating the likelihood of a domain ranking in search results based on link profile and trust signals.

A file that instructs search and AI crawlers how to access different parts of a website, controlling which pages can be crawled.

A set of performance metrics—LCP, FID and CLS—evaluating page loading, interactivity and visual stability

Optimizing content around people, places or things (entities) to help AI models understand relationships and context.

A search approach that interprets the meaning and context of queries, rather than relying solely on exact keyword matches.

AI search uses artificial intelligence to understand user intent and deliver conversational, context‑aware results beyond simple keyword matching.

BERT (Bidirectional Encoder Representations from Transformers) is Google's natural language processing algorithm that improves search by understanding context and nuance in queries.

AI overview is a concise, AI‑generated summary displayed at the top of Google search results that synthesises information from multiple sources.

RAG is an AI architecture that combines information retrieval with language model generation to produce up‑to‑date answers with citations.

Machine learning is a subset of artificial intelligence in which algorithms learn patterns from data to make predictions and decisions without being explicitly programmed.

Tokens are the fundamental units of text that AI models process, representing pieces of words, entire words or special symbols.

Embeddings are high‑dimensional numerical vectors that represent the semantic meaning and relationships of words, sentences or other data.

Prompt engineering is the practice of crafting and refining prompts to guide AI language models toward producing desired outputs.

Vector search is a semantic search technique that finds information based on meaning and context by converting content and queries into numerical vectors.

Technologies identifying whether content has been generated by AI systems rather than created by humans.

A large language model is a deep neural network trained on vast amounts of text data to understand and generate human‑like language for chatbots, search and other AI applications

Perplexity is an AI‑powered search engine that retrieves information from the live web and generates answers with citations in real time.

RankBrain is Google's machine‑learning algorithm that interprets search queries, especially new or ambiguous ones, to deliver relevant results.

AI training data consists of the large collections of text, images and other content used to train AI models to understand language and produce useful outputs.

ChatGPT is a conversational AI chatbot developed by OpenAI that uses large language models to answer questions, assist with tasks and generate content.

Anthropic is an AI safety company founded in 2021 by former OpenAI researchers and known for creating the Claude series of models that emphasise helpfulness, harmlessness and honesty.

Gemini is Google's advanced multimodal large language model that understands text, images and code and powers AI overviews and other services.

OpenAI is an AI research company founded in 2015 that created models like GPT‑3, GPT‑4, DALL‑E and ChatGPT, with a mission to ensure artificial general intelligence benefits all of humanity.

A context window is the maximum amount of text (measured in tokens) that an AI model can consider and remember during a single interaction.

Claude is an AI assistant created by Anthropic that prioritises helpfulness, harmlessness and honesty, offering strong reasoning and research capabilities.

Real‑time search refers to search engines that provide up‑to‑date information by retrieving current content from the web and other data sources beyond their static training data.

AI hallucination occurs when a language model generates plausible but false or fabricated information due to gaps in its training data or misinterpretation of context.

Natural language processing enables computers to understand, interpret and generate human language, powering chatbots, search engines and language tools.

Conversational search enables users to interact with search engines using natural language, follow‑up questions and contextual references instead of keywords.