Agentic commerce is shopping in which an AI agent acts for a person: it finds and compares products and can complete the purchase, sometimes without the shopper opening the merchant's website. Open protocols such as the Agentic Commerce Protocol from OpenAI and Stripe and Google's Universal Commerce Protocol give agents and merchants a shared way to handle the order.
AI brand mentions are the times an AI engine like ChatGPT or Google AI Overviews names your brand in an answer. A mention counts whether or not the answer links to your website. That's the difference from a citation, where the engine credits a specific page as its source.
AI citations are the links an AI engine attaches to its answer to show which web pages the information came from. Each citation credits a specific URL. A brand mention is different: the engine names a company in the text, even when it doesn't link to that company's site.
Also called: citation frequency, citation rate, LLM citations
AI referral traffic is website visits that arrive when someone clicks a link inside an AI-generated answer, such as a cited source in ChatGPT, Perplexity, Gemini, Claude, or Microsoft Copilot. Analytics tools identify it by the referring domain or by a UTM tag, like the utm_source=chatgpt.com parameter ChatGPT adds to its links.
AI share of voice is the percentage of all brand mentions in AI-generated answers that belong to your brand, measured across a fixed set of prompts. Each brand counts once per answer it appears in. If brands are named 200 times across your tracked answers and 50 of those mentions are yours, your share of voice is 25%.
An AI visibility score is the percentage of tracked AI answers that mention your brand. You divide the answers that name you by every answer in your tracked prompt set, including answers that name no brand at all. If your brand appears in 30 of 100 tracked answers, your AI visibility score is 30%.
A search tool that replies with a written answer instead of a list of links. ChatGPT, Perplexity, Gemini, and Google AI Overviews are all answer engines.
Branded prompts are AI questions that name your company, such as "Is Acme good for agencies?" Unbranded prompts describe a need without naming any brand, such as "best project software for agencies." Tracking unbranded prompts shows whether AI engines recommend you to buyers who don't know you yet.
Also called: branded prompts, unbranded prompts, non-branded prompts
Content chunking is the way AI search systems split a web page into smaller passages so each one can be retrieved and quoted on its own. The term also describes writing pages in clear, self-contained sections, so any passage an engine pulls out still makes sense without the rest of the page.
Also called: chunking, passage ranking, passage slicing
The maximum amount of text, measured in tokens, that a model can consider at once, including the prompt, any retrieved sources, and its own reply.
Engines and surfaces
Conversational search
Searching by asking questions in natural language and refining them through follow-ups, with the engine keeping the context of the conversation.
D
Disciplines
Domain authority
A score created by Moz that estimates how likely a domain is to rank, based mostly on its backlinks. It's a third-party metric that Google doesn't use.
Experience, expertise, authoritativeness, and trustworthiness: the qualities Google's quality rater guidelines use to judge whether content deserves to be trusted.
Also called: EEAT, experience, expertise, authoritativeness, trustworthiness
Coverage of your brand on sites you don't own, such as reviews, press, forums, and third-party lists. AI engines lean heavily on these sources when they recommend brands.
Embeddings are lists of numbers that represent the meaning of a piece of content, such as a paragraph of text or an image. Content with similar meaning gets similar numbers, so a system can find a relevant passage even when it shares no words with the question. Embeddings are what make semantic search and AI retrieval work.
Making sure search engines and AI models recognize your brand as a distinct entity, with consistent facts about what it is and what it's connected to.
Also called: entity optimization
F
Engines and surfaces
Featured snippet
A highlighted excerpt from one web page that Google shows near the top of the results to answer a query directly.
G
Engines and surfaces
Gemini
Google's family of AI models and the name of its standalone AI assistant. Gemini models also power AI Overviews and AI Mode.
Disciplines
Generative engine optimization (GEO)
Optimizing content to be included and credited in answers written by generative AI systems. In practice the term is used almost interchangeably with AEO.
OpenAI's crawler for collecting model training data. ChatGPT search uses a separate crawler, OAI-SearchBot, so blocking GPTBot in robots.txt doesn't remove you from ChatGPT search results.
Grounding in AI means tying a language model's answer to specific source material, such as live search results or a company's own documents, so the model isn't relying only on what it learned in training. A grounded answer can point to its sources, which is why AI search engines show citations.
Retrieval that combines keyword matching with vector search, so results match both the exact terms and the meaning of a query.
K
How AI search works
Knowledge cutoff
The date after which a model has no training data. Anything newer reaches the model only if it retrieves it from the web at answer time.
Also called: training cutoff
How AI search works
Knowledge graph
A database of entities and the relationships between them. Google's Knowledge Graph powers knowledge panels and helps AI systems resolve which brand a question is about.
L
How AI search works
Large language model (LLM)
An AI model trained on large amounts of text to predict and generate language. LLMs are the engines inside ChatGPT, Claude, Gemini, and most AI search tools.
llms.txt is a proposed standard for a markdown file at a website's root (example.com/llms.txt) that summarizes the site and links to its most useful pages for language models. Jeremy Howard of Answer.AI proposed it in September 2024. Google says its search ignores the file, and no other major AI search engine has said it uses one.
The Model Context Protocol (MCP) is an open standard for connecting AI applications to outside tools and data. An MCP server describes what it can do in a format any MCP-compatible assistant understands, so one integration can work across AI apps like Claude and Cursor without custom code for each.
Prompt tracking is running a fixed set of buyer questions through AI engines like ChatGPT and Perplexity on a schedule, then recording which brands and sources appear in each answer and where they rank. Comparing the same prompts over time shows whether your AI visibility is rising or falling.
Retrieval-augmented generation (RAG) is a technique where an AI model first searches a set of documents for passages that match a question, then writes its answer from those passages. Because it looks things up at answer time, the model can use up-to-date information and point to where it came from.
Semantic search is a way of finding information by matching the meaning of a query instead of its exact words. It lets a search engine or AI assistant return a page about "affordable CRM for startups" when someone asks for a "cheap customer database for a new company," even though the two phrases share almost no words.