The AI Search Dictionary

AI search is the umbrella name for search experiences in which a language model writes a direct answer, usually with cited links, from sources it retrieves or remembers, instead of or alongside a ranked list of results. It covers answer engines, AI layers inside classic search, and assistants that browse the web on request.

Generative engine (GE) is the academic name, coined in the 2023 GEO paper, for a search system that uses a large language model to gather information from several sources and write one answer to a query. Google AI Overviews, Perplexity and ChatGPT search are generative engines in this sense.

GEO (Generative Engine Optimization) is the practice of improving how often and how prominently content is used and cited in answers written by generative engines such as Google AI Overviews, ChatGPT search and Perplexity. The name comes from a 2023 research paper and is one of the most widely used labels for AI search work.

AEO (Answer Engine Optimization) is the practice of shaping content so that answer engines, voice assistants and AI systems can lift it as a direct answer to a question. The term predates large language models, first describing work on featured snippets and voice results, and now often serves as a synonym for GEO.

AIO is an acronym with three live meanings in search marketing: Google's AI Overviews, the common use in SEO tools and data studies; AI Optimization, an occasional name for the whole discipline; and AIOSEO, the All in One SEO plugin for WordPress. Reading a report correctly depends on knowing which one is meant.

LLMO (Large Language Model Optimization) is the practice of shaping a brand's presence so large language models describe it accurately and mention or cite it, whether they answer from training data or from live web retrieval. A practitioner coinage with no canonical definition, it is one of the field's three main names with GEO and AEO.

AI SEO is an ambiguous label with two common meanings: optimizing content and sites so AI search engines retrieve and cite them, a synonym for GEO; and using AI tools to do SEO work such as research, drafting and audits. Vendors use both, sometimes on the same page, so contracts should state which.

GAIO (Generative AI Optimization) is a name for the work of making content visible, cited and recommended in generative AI answers, used mainly by German-speaking agencies and publishers. It covers the same ground as GEO and has no canonical definition. Some glossaries list it as an alias of AI Optimization (AIO).

SXO (Search Experience Optimization) is the practice of optimizing the whole experience of a searcher, from the result they see to the page they land on and the action they take, by combining SEO with user experience and conversion work. It predates AI search and is occasionally stretched to cover AI answers.

AX (Agent Experience) is the experience AI agents have when they use a product, website or API on a person's behalf, and the work of making that experience good: easy access, clear APIs, machine-readable documentation and flows agents can complete. Mathias Biilmann of Netlify coined the term in January 2025.

Agentic SEO is a label with two uses: running SEO and GEO work through AI agents that plan, act and adapt across multi-step workflows under human review; and, less often, optimizing a site for AI agents that search, compare and act for users, which overlaps with AX. Ahrefs and Promptwatch both use the first meaning.

AI visibility is the state the whole field aims at: whether, where and how a brand, site or fact appears in AI-generated answers across engines, measured mainly as mention rates and citation rates on a fixed set of prompts. Teams often prefer it as a neutral name that sidesteps the GEO, AEO and LLMO dispute.

Search Everywhere Optimization is a marketing label for optimizing a brand's organic presence on every platform where people look for answers, including Google, YouTube, TikTok, Reddit, Amazon and AI assistants such as ChatGPT, instead of on Google alone. Neil Patel and his agency NP Digital promote it, notably in a January 2025 article.

Conversational search is a way of searching in natural-language turns, where the system keeps the context of earlier questions, can ask or suggest follow-ups, and answers in prose rather than returning a fresh list of links each time. It is the default interaction mode of AI assistants and of Google's AI Mode.

Zero-click search is a search that ends without the user clicking through to any website, either because the results page, an AI answer or a feature such as a snippet or map already gave the answer, or because the user gave up. In AI search it names visibility that earns exposure without a visit.

Agentic commerce is shopping in which an AI assistant or agent does part of the buying work for a person: finding products, comparing offers, filling a cart and, in some systems, paying at checkout. It turns AI answers into a sales channel where product data and checkout integrations decide which merchants get recommended and chosen.

Agent-Led Growth (ALG) is a go-to-market idea named after product-led growth, used in two senses: growth that comes when AI agents discover, evaluate, recommend or buy products for people, and growth driven by companies that deploy their own agents to sell. The first sense is the one that concerns AI search.

Google AI Overviews are the AI-generated summaries shown at the top of some Google results pages, written by a custom Gemini model and paired with links to the web pages that support them. They grew out of the Search Labs experiment known as SGE and are available in more than 200 countries and territories.

Google AI Mode is the conversational AI search experience inside Google Search, reached through its own tab, where people ask long or multi-part questions and follow up in a chat-style thread. Answers are generated by Gemini models with links to supporting pages, and since 2026-05-19 Gemini 3.5 Flash is the default model worldwide.

Gemini is Google's standalone AI assistant, used at gemini.google.com and in mobile and desktop apps, and renamed from Bard on 2024-02-08. It answers from its model's knowledge and, for many questions, from web information; when it provides links, a Sources button opens a panel listing them. It is separate from Google Search and its AI features.

ChatGPT search is the web search capability built into OpenAI's ChatGPT: when a question needs current or specific information, ChatGPT searches the web, writes an answer and attaches citations and a Sources list. It launched on 2024-10-31, absorbing the SearchGPT prototype, and is available on every ChatGPT plan and to signed-out users.

SearchGPT was the name of OpenAI's temporary AI search prototype, announced on 2024-07-25 and tested with a small group of users and publishers. Its features were folded into ChatGPT when ChatGPT search launched on 2024-10-31, so the name now survives mainly in 2024-era articles and as an informal synonym for ChatGPT search.

Microsoft Copilot is Microsoft's consumer AI assistant, launched as Bing Chat on 2023-02-07 and renamed Copilot in late 2023, with answers grounded in Bing search results and linked to their sources. Bing also offers Copilot Search, a results page launched on 2025-04-04 that blends classic results with a generated answer and prominently cited sources.

Perplexity is an AI-native answer engine that responds to questions with answers synthesized from live web retrieval, marking claims with numbered citations and listing its sources. First released as Ask in December 2022, it now spans free and paid search, Deep Research, the Comet browser, the Computer agent and a developer API platform.

Claude is Anthropic's AI assistant. Its web search, launched on 2025-03-20 and available on all Claude plans worldwide since 2025-05-27, lets Claude look up current information during a conversation and answer with citations to the pages it used. Anthropic's help center says every web-search response includes citations so users can verify sources.

Grok is the AI assistant built by xAI, available inside X and as a standalone app at grok.com. It can search both the open web and posts on X, and its DeepSearch mode, introduced with the Grok 3 beta on 2025-02-19, runs a research agent that weighs conflicting sources and returns a concise report.

Meta AI is Meta's AI assistant, built into WhatsApp, Instagram, Facebook and Messenger and available as the Meta AI app and at meta.ai. Launched broadly with Llama 3 on 2024-04-18, it has run on Meta's proprietary Muse Spark model since 2026-04-08 and is adding web research, shopping and planning features.

You.com is a company that began as an AI-assisted search engine and launched YouChat, one of the first chat assistants built into a search engine with current, cited answers, in December 2022. Its site now presents it as search and inference infrastructure for AI, selling web search, contents, answer and research APIs to developers and enterprises.

Brave Search and Leo are Brave's two AI surfaces. Brave Search runs on its own independent web index, with AI Answers that summarize results with cited sources and Ask Brave, a chat mode with follow-ups and Deep Research. Leo, the assistant inside the Brave browser, launched on desktop on 2023-11-02. The Brave Search API sells access to the index.

Mistral Le Chat is the AI assistant of the European model maker Mistral AI, launched in beta on 2024-02-26 and offering web search with detailed citations by 2025-02-06. In May 2026 Mistral renamed it Vibe, merging the chat assistant with work-automation and coding agents; existing conversations and plans carried over.

DeepSeek is a Chinese AI lab whose free chat assistant, on the web at chat.deepseek.com and in mobile apps launched on 2025-01-15, offers a web search option and a reasoning mode. Its open-weight models, from R1 (2025-01-20) to the V4 family (preview 2026-04-24), power the assistant, and an in-house search engine backs its Search API.

Amazon Rufus was Amazon's generative AI shopping assistant, announced in beta on 2024-02-01 and later offered to all US customers in the Amazon Shopping app and on desktop. On 2026-05-13 Amazon renamed it Alexa for Shopping, merging Rufus with Alexa+. It answers product questions from Amazon's catalog, reviews, community Q&A and the web.

Apple Intelligence and Siri form Apple's AI system for iPhone, iPad and Mac, first released on 2024-10-28. Its rebuilt assistant, Siri AI, announced on 2026-06-08 and rolling out in beta from 2026-09-14, answers questions using information from across the web, and Apple says such answers may include links to sources.

Sources panel, or citation UI, names the part of an AI answer interface that shows which web pages the answer relied on, through inline citation markers, numbered references, link cards or a side panel listing cited and related pages. It is where a citation becomes visible and clickable, and every engine designs it differently.

AI Overview link carousel is the common name for a horizontally scrolling row of link cards attached to a Google AI Overview or AI Mode answer, separate from the links placed inline in the text and from the link list shown beside the answer on desktop. Google documents article carousels for some news and developing-topic answers, highlighting Preferred Sources.

Featured snippet is the name of a Google search result shown in a special box where the usual order is reversed: a passage extracted from one page, answering the query, appears first, followed by that page's title and link. Google's systems choose the page automatically. It is the single-source ancestor of today's multi-source AI answers.

Knowledge panel is the name of the information box Google shows when someone searches for an entity in its Knowledge Graph, such as a person, place, organization or thing. Google's systems generate it automatically from sources across the web and from data partners, and the entity's official representatives can claim it and suggest changes.

People Also Ask is Google's expandable group of questions related to a search, shown on the results page; expanding a question reveals a short answer, shown as a featured snippet from one page. Google's documentation calls it a related questions group. For AI search work it is a free, visible sample of the questions people ask around a topic.

ChatGPT Atlas was OpenAI's AI-native web browser, launched on macOS on 2025-10-21 with a ChatGPT sidebar that could read the open page and an agent mode that could browse on the user's behalf. OpenAI announced its deprecation on 2026-07-09 and switched it off on 2026-08-09, moving those capabilities into ChatGPT and Codex.

Perplexity Comet is the web browser made by Perplexity, launched on 2025-07-09 for Max subscribers, free to everyone since 2025-10-02 and available on Windows, Mac, Android and iOS since March 2026. Its built-in Comet Assistant explains and compares pages and can act for the user, for example by booking a meeting or sending an email.

Apps in ChatGPT are third-party applications that users can call inside a ChatGPT conversation, with their own interface shown in the chat. OpenAI launched them on 2025-10-06 with the Apps SDK, built on the Model Context Protocol, opened an app directory to developer submissions in December 2025 and folded it into a Plugin directory on 2026-07-09.

Deep Research names a category of agentic research modes in AI assistants that plan a multi-step investigation, run many searches, read large numbers of pages and return a long, cited report after several minutes. Google's Gemini Deep Research (2024-12-11) and OpenAI's deep research (2025-02-02) were early examples, and most major assistants now offer one.

Google Preferred Sources is a Search setting that lets people pick news sites and other publishers they want to see more often. Launched on 2025-08-12 in the US and India for Top Stories, it reached all supported languages from 2026-04-30 and, since 2026-05-27, marks links from chosen sites as "Preferred" in AI Overviews and AI Mode.

Computer-use and browsing agents are AI systems that operate a browser or a whole computer for a user: they view the screen, click, type, fill forms and move between pages to finish a task. Examples include Claude's computer use and Claude in Chrome, Gemini in Chrome's auto browse, Copilot in Edge, Comet's assistant and ChatGPT's cloud browser.

Gemini in Chrome is Google's AI assistant built into the Chrome browser, opening in a side panel that can read the current page and other open tabs. Its auto browse feature, launched in preview on 2026-01-28 for US AI Pro and Ultra subscribers, lets Gemini carry out multi-step tasks on websites, pausing for confirmation before purchases.

ChatGPT shopping is the product-discovery experience inside ChatGPT: when a request shows buying intent, ChatGPT can show product results with images, prices and merchant links, drawn from merchant feeds, partner catalogs and crawled web content. Purchases complete on the merchant's own site or app since OpenAI moved away from in-chat Instant Checkout in 2026.

Visual search is searching with an image instead of, or alongside, typed words: pointing a phone camera, uploading a photo or circling something on screen, then getting results or an AI answer about what is shown. Google reported on 2026-10-28 more than 50 billion visual searches per month, and one in five AI Mode searches use voice, images or documents.

Retrieval-augmented generation (RAG) is a way of building AI answers in which the system first fetches relevant documents from an index, then has a language model write its response using those documents as evidence. It lets an assistant answer from current, citable sources instead of relying only on what the model memorized during training.

Grounding is the step where an AI system ties its answer to specific, checkable sources supplied at the moment it responds, such as search results or a company's own documents. A grounded answer can show where each claim came from; an ungrounded one relies only on what the model learned during training.

Query fan-out is a technique in which an AI search system turns one user question into many related searches, runs them across subtopics and data sources, and combines what it finds into a single answer. Google documents that both AI Overviews and AI Mode may use it, so pages can be retrieved for queries the user never typed.

Sub-document retrieval (passage-level retrieval) is a search method that finds and returns the specific section of a page that answers a question, instead of treating the whole page as one unit. Engines that work this way can quote one strong paragraph from a long article and ignore the rest of the page.

Chunking is the step in a retrieval pipeline that splits long documents into smaller pieces, called chunks or passages, before they are indexed and searched. Each chunk is retrieved on its own, so where the boundaries fall decides whether a passage carries a complete, understandable answer or a fragment cut off from its context.

Embedding is the conversion of text into a list of numbers, a vector, positioned so that passages with similar meanings sit close together. AI search systems embed both questions and documents, which lets them match a question to a page that answers it even when the two share few or no words.

Vector search is a retrieval method that finds the stored items whose embeddings are closest to the embedding of a query, instead of looking for matching words. It lets an AI search system pull passages that mean the same thing as the question, and at large scale it relies on approximate algorithms that trade a little accuracy for speed.

Cosine similarity is a measure of how closely two vectors point in the same direction, computed as the cosine of the angle between them. In AI search it scores how alike two embeddings are in meaning, from 1 for the same direction to -1 for opposite, regardless of the vectors' length.

Bi-encoder is a retrieval model architecture that encodes a query and a document separately into vectors in the same space, then compares the vectors. Because documents can be encoded once in advance, a search system only has to encode the query at search time, which makes fast retrieval over millions of passages possible.

Cross-encoder reranking is a second retrieval stage in which a model reads the query and each candidate passage together and scores how well the passage answers the query. It is more accurate but much slower than first-stage retrieval, so it is applied only to a shortlist, deciding which few passages reach the language model.

Hybrid search is a retrieval approach that runs keyword search and vector search on the same query and merges the two result lists into one ranking. It catches exact matches, such as product names and error codes, and passages that match the meaning of a question in other words; each method alone tends to miss one of the two.

BM25 is a classic keyword ranking formula that scores a document by how often it contains the query's words, how rare those words are across the collection, and how long the document is. Decades old, it remains the standard lexical baseline and still runs inside many hybrid AI retrieval systems.

Reciprocal rank fusion (RRF) is a simple method for merging several ranked result lists into one. Each document earns points from its position in each list, one divided by the sum of a constant and its rank, and the points are added up. Documents that rank well in several lists rise to the top, with no training required.

Training data vs retrieval data is the distinction between the two routes by which web content reaches an AI answer: absorbed into a model's weights during training, where it surfaces without a source, or fetched from an index when someone asks, where it can be cited. Major AI companies run separate crawlers and controls for each.

Knowledge cutoff is the date after which a language model's training data contains little or no information. Anything newer can reach an AI answer only through retrieval, such as a web search at question time. Labs publish cutoffs inconsistently, and some now distinguish the date of reliable knowledge from the end of the training data.

Parametric memory vs grounded answers is the distinction between an AI answer generated from what a model absorbed during training and one built from documents retrieved when the question was asked. Grounded answers can be checked against their sources and updated by changing those sources; answers from parametric memory carry no source and change only with retraining.

Hallucination is when an AI model produces content that sounds plausible and confident but is false or unsupported by its sources, such as an invented feature, statistic or citation. Grounding answers in retrieved documents reduces hallucination without eliminating it, and in AI search it shows up as wrong statements about products, people and companies.

Citation selection is the step in which an AI answer engine decides which of the sources it retrieved will appear as citations or links in the final answer. It is the least transparent stage of AI search: engines show the result but publish no weights, so every list of citation factors is inferred.

Source diversity (link-set diversity) is Google's stated aim for its AI features to show a wider and more diverse set of supporting links than a classic results page. In principle it leaves room for pages outside the top classic results to be cited in AI Overviews and AI Mode for a specific angle of the answer.

Answer synthesis is the generation step in AI search where a language model reads the passages that retrieval selected and writes them into one coherent answer, usually with citations attached. The model decides what to include, in what order and in which words, so the same sources can yield different answers in different engines.

Context window is the maximum amount of text, measured in tokens, that a language model can consider at once, including its instructions, the conversation, any retrieved documents and its own reply. In AI search it caps how much retrieved material an answer can draw on, which gives engines a reason to pass selected passages rather than whole pages.

Prompt injection is an attack in which text crafted to act as instructions changes how an AI model behaves. In the indirect form, the instructions are planted in a web page or file that an AI system later reads, for example hidden text telling an answer engine to recommend a product. Security standards classify it as a vulnerability.

Agentic search is search carried out by an AI agent that plans its own sequence of queries, reads and compares the results, refines its next searches based on what it found, and sometimes acts on the results, before producing an answer or finishing a task. One user request can trigger dozens or hundreds of searches.

An AI crawler is any automated agent that fetches web pages for an AI system. The family has three roles: training crawlers that gather corpora for models, search crawlers that build the index answer engines retrieve from, and user-triggered fetchers that load one page because one person asked. Most large operators publish a separate token for each role.

GPTBot is OpenAI's training crawler. It fetches public web pages that may be used to train OpenAI's generative foundation models, and a robots.txt rule disallowing the GPTBot token tells OpenAI not to use a site's content for that training. It is controlled separately from OAI-SearchBot, the agent that governs inclusion in ChatGPT search.

OAI-SearchBot is OpenAI's search crawler. It fetches pages so they can be surfaced and linked in ChatGPT's search features. Sites that disallow it are left out of ChatGPT search answers, though they can still appear as navigational links. It is the token that governs ChatGPT search visibility, independent of the training crawler GPTBot.

ChatGPT-User is OpenAI's user-triggered fetcher. It visits a web page when a ChatGPT user or a Custom GPT needs that page to answer a question, and it handles GPT Actions calls. OpenAI says it does not crawl automatically, that robots.txt rules may not apply to it, and that it plays no part in deciding search inclusion.

OAI-AdsBot is OpenAI's ads crawler. When an advertiser submits an ad on ChatGPT, it visits the landing page to check it against OpenAI's policies and may use the page's content to decide when the ad is relevant. It visits only pages submitted as ads, and OpenAI states its data is not used to train foundation models.

PerplexityBot is Perplexity's search crawler. It fetches pages so Perplexity can surface and link them in its search results, and Perplexity states it is not used to crawl content for AI foundation models. Sites that want to appear in Perplexity answers are told to allow it in robots.txt and to permit its published IP ranges.

Perplexity-User is Perplexity's user-triggered fetcher. When someone asks Perplexity a question, it may visit a web page to help answer and link that page in the response. Perplexity says it is not used for crawling or for collecting training data, and that because a user requested the fetch, it generally ignores robots.txt rules.

ClaudeBot is Anthropic's training crawler. It collects public web content that could contribute to training Anthropic's generative AI models, and restricting it in robots.txt signals that a site's future content should be excluded from training datasets. Visibility in Claude's web search answers is governed separately, by Claude-SearchBot and Claude-User.

Claude-User and Claude-SearchBot are Anthropic's two non-training robots. Claude-SearchBot crawls the web to improve the relevance and accuracy of Claude's search results, and Claude-User fetches pages when a person's question to Claude needs them. Both follow robots.txt, and Anthropic warns that disabling either may reduce a site's visibility in Claude's answers.

Google-Extended is a robots.txt product token, with no crawler of its own, that lets publishers decide whether content Google crawls may be used to train future Gemini models and to ground answers in Gemini Apps and Vertex AI. Google states that it does not affect inclusion or ranking in Google Search, AI Overviews included.

Googlebot is Google's main web crawler, with smartphone and desktop variants that share one robots.txt token. It feeds the Google Search index, which supplies the supporting links in AI Overviews and AI Mode. Google states that robots.txt rules for Googlebot are the control for how a site is crawled for Search, AI features included.

Bingbot is Microsoft's standard web crawler for the Bing index. Microsoft documents that its Copilot products in Microsoft 365 ground web answers by sending generated queries to the Bing search service, and Bing reports Copilot citations per site, so Bingbot's crawl is the main path into Copilot's web answers. Page tags, rather than a separate token, govern AI uses.

Applebot and Applebot-Extended are Apple's two robots.txt tokens. Applebot is the web crawler behind search in Spotlight, Siri and Safari, and its data may also train Apple's foundation models. Applebot-Extended crawls nothing: disallowing it opts content out of that training, while the nosnippet directive keeps content out of Apple's AI-generated answers.

Meta-ExternalAgent, Meta-ExternalFetcher and Meta-WebIndexer are Meta's main AI-related crawler tokens. ExternalAgent crawls for uses such as model training and direct indexing; WebIndexer improves Meta AI search and helps Meta cite content; ExternalFetcher fetches links at a user's request and may bypass robots.txt. A fourth token, Meta-ExternalAds, serves advertising products.

Bytespider is the web crawler operated by ByteDance, the company behind TikTok, and Cloudflare classifies it as an AI crawler. No ByteDance crawler policy could be read for this entry, so what is known about its purpose, volume and robots.txt handling comes from infrastructure providers' observations instead of operator documentation.

Amazonbot, Amzn-SearchBot and Amzn-User are Amazon's three web crawlers. Amazonbot improves Amazon products and may train Amazon AI models; Amzn-SearchBot powers search experiences such as Alexa and is not used for generative AI training; Amzn-User fetches live information for user requests. Each has its own robots.txt setting and published IP list.

CCBot and Common Crawl are the crawler and the nonprofit behind a free, open archive of web crawl data. That archive is a widely used ingredient in large language model training, so disallowing CCBot acts as an upstream training opt-out, but only for future crawls: archives already published stay in circulation.

Crawl propagation delay is the lag between changing a crawl rule or a page and a crawler or index reflecting the change. Two lags hide under the term: how long a crawler keeps using a cached robots.txt, usually up to 24 hours under RFC 9309, and how long an engine takes to recrawl a changed page, from days to months.

IP-verified crawlers are bots whose operators publish the IP ranges or reverse-DNS patterns they crawl from, so site owners can confirm that a request claiming to be GPTBot, Googlebot or ClaudeBot really comes from that operator. Because user-agent strings are easy to fake, address or DNS checks are the dependable basis for allowlists and blocks.

Passage ranking is a Google Search system that identifies individual sections, or passages, of a web page to judge more precisely how relevant the page is to a query. Announced in October 2020 and live for US English queries in February 2021, it lets one precise answer buried deep in a long page help that page rank.

RankBrain is a Google Search AI system, launched in 2015, that helps Google understand how words relate to concepts, so it can return relevant pages even when they lack the exact words of a query. Google calls it its first deep learning system in Search and says it still helps rank top results.

BERT, short for Bidirectional Encoder Representations from Transformers, is a language model Google applied to Search in October 2019 to understand how combinations of words express meaning and intent. Reading each word in the context of the words around it, it helped Search handle small words like "to" that change what a query means.

Crawl budget is the set of URLs a search engine can and wants to crawl on a site, set by how much load the server can take (crawl capacity) and how much the engine wants the content (crawl demand). In AI search, fetches wasted on duplicate or low-value URLs leave fewer for the pages that should be cited.

Backlinks are links from other websites to a page, the classic authority signal that Google and Apple both document in their search ranking. In AI search their role is indirect: a page usually has to be retrieved through a search index before it can be cited, and one benchmark suggests strong content can partly offset weak link authority.

Co-citation and co-occurrence describe things appearing together: co-citation when a third source cites two documents together, co-occurrence when two terms or names appear in the same context. In AI search, the hypothesis is that a brand mentioned alongside a topic across many independent sources becomes associated with that topic in what models learn.

Information gain, in search, is the idea of scoring a document by how much new information it adds beyond what a user has already seen. Google holds a patent on the technique, granted in 2022, and SEO practitioners cite it to explain why derivative content underperforms. Google has not confirmed using such a score in Search or AI answers.

A reasoning model is a language model trained to work through a problem in intermediate steps, using internal reasoning tokens, before it gives a final answer. OpenAI's o-series and DeepSeek-R1 (January 2025) made the class widely known. Reasoning does not imply web access: search is a separate tool that products may connect to these models.

Model temperature is a sampling setting that controls how random a language model's word choices are: low values make output more focused and repeatable, high values more varied. It is one reason AI answers can differ between runs, but variation persists even at temperature zero, and some APIs no longer expose the setting at all.

A vector database stores embeddings, numeric vectors that represent the meaning of text or other content, and finds the stored vectors closest to a query vector. It is the usual home of the retrieval step in retrieval-augmented generation: content is chunked, embedded, stored, and searched at answer time to pick the passages a model grounds on.

Google-Agent is the user agent Google uses when AI agents running on its infrastructure browse websites and take actions at a user's request. Google lists it among its user-triggered fetchers, which generally ignore robots.txt because a person asked for the visit, and it is testing Web Bot Auth signatures under the identity agent.bot.goog.

AI recommendation poisoning is a manipulation technique in which a website or email plants instructions in an AI assistant's memory, usually through a 'Summarize with AI' link carrying a prefilled prompt, telling it to treat a company as a trusted source or recommend it first. Microsoft named the practice in February 2026 after finding real businesses using it.

Cite-worthy statistics are specific, checkable numbers placed in content, such as a dated measurement with its source, that an AI answer engine can lift to support a claim. The GEO paper's "Statistics Addition" method was one of its top three tactics, but the evidence comes from one benchmark study that measured pages already selected as sources.

Citing sources inside your content means naming, and usually linking, the reliable sources behind your claims within the page itself. The GEO paper's "Cite Sources" method was one of its three strongest tactics on its benchmark and helped lower-ranked pages most, but the evidence is one study whose results varied by metric and engine.

Quotations and expert voices are attributed statements from named, credible people or organizations included in a page. In the GEO paper, "Quotation Addition" was the best single method on its benchmark and on Perplexity's word-count metric, one study suggesting that attributed, distinctive statements are easy for answer engines to reuse.

Fluency and readability optimization is rewriting a page so its prose is smooth, well organized and simple to follow, without changing the facts. In the GEO paper, "Fluency Optimization" and "Easy-to-Understand" raised visibility by 15 to 30% on its benchmark, one study suggesting presentation affects how much of a source an engine reuses.

Keyword stuffing is repeating query terms, or lists of keywords, in a page to force relevance, a classic SEO manipulation. In the GEO paper it gave little to no gain and performed about 10% worse than baseline on Perplexity's word-count metric, and Google's spam policies, which apply to its generative AI responses, prohibit it.

Authoritative tone and technical language are style changes that make a page sound more persuasive and expert, or add domain terms. In the GEO paper, the "Authoritative" and "Technical Terms" methods produced small, uneven effects: the authors found no significant gain from persuasive tone on their benchmark, and results varied by domain and engine.

Domain-specific optimization is the principle that the content tactics that raise visibility in AI answers differ by topic and query type, so no single recipe works everywhere. The GEO paper found each method had different best-performing categories, such as statistics for law and government queries and quotations for questions about people and society.

Answer capsules, also called answer blocks, are short, self-contained passages, often one to three sentences, placed right under a question or heading and written so an AI engine can lift them whole as an answer. Microsoft's Bing team recommends such snippable answers; Google says no special formatting is required, and no study has measured the effect.

Content extractability is how easily a machine can pull complete, accurate statements out of a page. It depends on key information being present as text in the HTML, instead of locked inside images, PDFs, hidden tabs or script-only rendering. Google and Microsoft both document the underlying practices for their AI features.

Declarative headings are headings that state a section's point, such as "Most AI crawlers skip JavaScript", instead of teasing it. They give readers and machines an accurate label for each passage. Microsoft recommends descriptive headings for AI answers; the extra benefit of headings that state a claim is an untested inference.

Question-based headings are H2 or H3 headings phrased as the questions people actually ask, such as "How long does a robots.txt change take to apply?", each followed by a direct answer. Microsoft recommends question-and-answer formats for AI answers, and Pew found Google showed AI summaries on 60% of searches starting with a question word.

Semantic HTML is markup that uses elements for their meaning, such as headings, lists, tables, buttons and captions, instead of generic styled containers. It lets parsers, screen readers and AI agents see a page's structure without rendering it visually. Google calls it a good idea but not a requirement; its effect on AI citations is unmeasured.

JavaScript rendering is the question of whether a page's content exists in the HTML the server sends or appears only after JavaScript runs in a browser. Googlebot renders JavaScript, but a December 2024 analysis of Vercel's network found that the major AI crawlers it observed did not, so client-only content may be invisible to them.

Structured data is machine-readable markup, usually JSON-LD using the schema.org vocabulary, that labels what a page and its entities are. Google states AI Overviews and AI Mode need no special schema, while existing markup must match the visible text. Microsoft says schema helps AI systems interpret pages. Neither presents it as a citation shortcut.

llms.txt is a proposed Markdown file at a site's root that gives AI agents a short description of the site and links to clean Markdown versions of key pages. Proposed by Jeremy Howard in September 2024, it is read by some agents and tools on demand. Google states Google Search ignores it, and no engine documents using it for citations.

Content freshness and dating means keeping pages current and showing clear publication and update dates, so engines and readers can judge whether information still holds. A 2025 Ahrefs vendor study found AI assistants cited somewhat newer pages than Google's organic results, ChatGPT most of all; no engine publishes how it weighs freshness.

Original research and data studies are first-party findings a publisher produces itself, such as surveys, benchmarks, experiments or usage data, which make its site the primary source for a fact. Google's 2026 guidance names unique, non-commodity content as the most influential factor for its AI features; no study has measured a citation advantage.

Internal linking and discoverability means connecting a site's pages with crawlable links and descriptive anchor text so crawlers can find every page worth citing. Google lists findability through internal links among the SEO fundamentals for AI Overviews and AI Mode, and says every important page should be linked from at least one other page.

Page experience and crawlability basics are the technical floor for appearing in AI answers: crawlers allowed by robots.txt, CDNs and firewalls, pages indexed and eligible for snippets, and a usable page. Google says its AI features add no technical requirements beyond these, apart from a Search Console control that includes sites by default.

Multimodal content means supporting a page's text with relevant images and video, each made machine-readable through alt text, captions, nearby text and metadata. Google recommends high-quality images and video for its AI features, which can show visual results, and Microsoft advises alt text or HTML for critical image details. No engine publishes an effect size.

Multilingual and hreflang coverage means publishing language and regional versions of key pages and declaring them with hreflang, so search systems can send each user to the right version. Google documents hreflang for Search, which its AI features draw on; how language shapes AI citations is undocumented, and vendor data suggests ChatGPT often searches in English.

Local and business-profile signals are the business facts engines use in local and commercial answers: Google Business Profile details such as category, address, hours and reviews, Merchant Center product data, and consistent name, address and phone listings. Google lists up-to-date Business Profile and Merchant Center information among its best practices for AI features.

Entity SEO is the practice of making a brand, person, product or concept recognizable to search systems as one distinct thing with stable facts, instead of a string of words. It relies on consistent naming, disambiguating descriptions, structured data and corroborating third-party sources, so that engines and the AI answers built on them resolve the right entity.

Brand entity means the machine-readable identity of a company or product: its canonical name, logo, description, official website and profiles, plus core facts such as founders, founding date and category. Search systems and AI answers rely on that identity to decide which organization a question is about and which facts to state about it.

Google Knowledge Graph, usually called the Knowledge Graph, is Google's database of facts about people, places, organizations and things, and of the relations between them. Introduced in 2012, it powers knowledge panels and helps Google interpret what a query or page is about. Its facts come from public sources, licensed data and content owners who claim their panels.

sameAs is a schema.org property that links an entity described on a page to other web pages that unambiguously identify the same entity, such as its Wikidata item, Wikipedia article or official social profiles. It helps search systems reconcile scattered mentions into one identity for an organization, a person or a product.

Wikipedia presence means a brand, person or product being covered accurately on Wikipedia, in its own article or within existing ones. It matters for AI search because Wikipedia is among the most cited sources in AI summaries and is widely reused as reference data, but an article has to be earned through independent coverage; the subject cannot simply write one.

Wikidata is the free, multilingual knowledge base run by the Wikimedia community that stores facts as structured statements about items, each with a permanent identifier such as Q42. Its data is dedicated to the public domain (CC0), so search engines, assistants and data projects can reuse it, which makes an accurate item a widely shared reference for an entity.

E-E-A-T stands for experience, expertise, authoritativeness and trustworthiness, the qualities Google's search quality raters use to judge whether content is credible, with trust named as the most important. Google presents E-E-A-T as a guide to what its systems should reward, with extra weight on health, finance and safety topics, and states that it is not a specific ranking factor.

Author authority and bylines together form the visible, checkable evidence that a named person with relevant expertise wrote or reviewed a piece of content: a byline, a bio, credentials and a track record elsewhere. Bylines and author markup make that evidence readable by people and machines, though no engine documents weighting individual authors when it chooses citations.

Digital PR for AI answers is the work of earning coverage of a brand in independent publications and reference sites that AI search engines already cite, so the brand appears in answers through third-party sources as well as through its own site. One 2025 study found that AI search services lean heavily on such earned media.

Brand mention seeding is the deliberate placement of brand mentions across forums, Q&A sites, comment threads and similar spaces in the hope of influencing what AI models learn and retrieve. Its efficacy is unproven, and when done covertly or at volume it breaches platform spam rules and Google's spam policies, which makes it a reputational risk.

UGC platforms as cited sources (Reddit, Quora, forums) describes the role of discussion sites, where users write the content, in AI answers. Engines cite them often for opinions, comparisons and first-hand experience, and both Google and OpenAI have data partnerships with Reddit. How a brand is discussed on these platforms can shape how AI answers describe it.

Reviews platforms (G2, Trustpilot, Capterra) are third-party sites where customers rate and review products and companies. AI answers to "best X" and comparison questions often draw on them, and shopping assistants use review content directly, so a brand's ratings and review text there feed into how it is described.

YouTube as a cited source refers to videos linked or summarized in AI answers. In Pew's 2025 study of Google AI summaries, YouTube was one of the three most-cited sources, with Wikipedia and Reddit. Captions, transcripts, titles and descriptions give text-based retrieval something to read, though no engine documents how it processes video.

Topical authority is the degree to which a site covers a subject completely, accurately and coherently enough to be treated as a go-to source on it. In AI search it matters because one question fans out into many sub-queries, and a site with strong pages across the whole topic gives more of those sub-queries a passage worth citing.

Content clusters / hub structure is the practice of grouping pages on one subject around a hub, or pillar, page that covers the topic broadly and links to deeper pages on each subtopic, which link back. It is the site-architecture form of topical authority, helping crawlers find every page and helping engines match sub-questions to specific passages.

AGENTS.md is an open Markdown file format that gives AI coding agents project-specific instructions, such as how to build, test and follow conventions, placed at a repository's root or in subfolders. Released by OpenAI in August 2025 and now stewarded by the Linux Foundation's Agentic AI Foundation, it serves agents working on code; no answer engine documents reading it.

NLWeb is an open project from Microsoft, launched on 2025-05-19, that lets a website answer natural-language questions about its own content through a simple protocol, returning results as Schema.org JSON. Every NLWeb instance also acts as a Model Context Protocol server, so one endpoint can serve people in a chat box and AI agents alike.

Scaled content abuse is Google's spam-policy term for generating many pages primarily to manipulate search rankings instead of helping users, whether the pages are written by AI, by people or by both. Google's spam policies now also cover attempts to manipulate generative AI responses in Search, so mass-produced pages are a risk in AI search too.

Product feeds and AI shopping visibility concern the structured catalog files (titles, prices, availability, images, identifiers) that merchants submit to platforms such as Google Merchant Center or OpenAI's commerce program. AI shopping surfaces use them to find, describe and recommend products and, through new agentic checkout protocols, to let assistants complete purchases. Stale feeds lead to wrong prices or exclusion.

Advertising in AI answers means paid placements shown inside, beside or below AI-generated responses, such as ads in Google's AI Overviews and AI Mode, sponsored units in ChatGPT and ads in Microsoft Copilot. They are bought through each platform's ad system and labeled as sponsored, separately from the organic citations an answer earns.

Context position is where a retrieved passage sits within the text a language model reads before it answers. Research shows models use information at the start or end of a long context more reliably than information in the middle. Publishers cannot choose that slot, but self-contained passages remain usable wherever retrieval places them.

LLM seeding is the practice of placing a brand's information in the places and formats AI systems are likely to retrieve, summarize and cite, such as review sites, forums, industry publications and comparison articles, instead of relying only on the brand's own website. It is an off-site visibility tactic whose effects remain poorly measured.

Self-promotional listicles are 'best X' roundup articles in which the publishing company ranks its own product first, often written to be cited when AI assistants answer comparison questions. They still earn AI citations, but practitioners linked them to Google visibility losses in early 2026, which makes them a contested tactic.

AI Max for Search is a Google Ads setting, announced on 2025-05-06, that adds AI-driven keyword expansion, generated ad text and automatic landing-page selection to a Search campaign. It matters for AI search because it qualifies ads for contextual matching inside AI Overviews and AI Mode, which exact and phrase match keywords mostly cannot reach.

Direct Offers is a Google Ads pilot, announced on 2026-01-11, that lets advertisers attach exclusive deals such as discounts to their products so Google's AI can present them to shoppers in AI Mode. Since May 2026 offers can surface directly within AI Mode responses, and offer types are widening beyond discounts.

An AI visibility score is a vendor-defined number that summarizes how present a brand is in AI-generated answers across a set of tracked prompts and engines. Each tool computes it differently, often blending appearance frequency with prominence, sentiment or competitor share, so a score is only readable alongside that tool's formula.

Citation share is the proportion of sampled AI answers, over a fixed set of prompts and engines, that cite a given domain at least once. It is the AI search counterpart of share of voice, and it is only comparable between reports that hold the prompt panel, engines, locale, sampling count and dates constant.

Citation rate is how often a specific page or domain is cited when a given question is asked, usually measured on one engine over repeated runs. It is narrower than citation share, and its meaning depends entirely on the denominator: per page or per domain, per query or per panel, per run or per day.

Citation prominence describes where and how visibly a source appears inside an AI answer: an inline link early in the text, a footnote at the end, or a collapsed sources panel. The GEO paper formalized one version, position-adjusted word count, which measures how much of the answer's text is attributed to a source, weighted toward the top.

Brand mention rate is the share of sampled AI answers that name a brand, whether or not they link to it. It captures awareness that citation counts miss, but an uncited mention carries no click path, and the answer alone cannot show whether the name came from training data or from retrieved pages.

Sentiment of AI answers is the automated classification of how AI-generated answers describe a brand: positive, neutral or negative overall, and often per attribute such as price, quality or support. Monitoring tools offer it to catch stale, inaccurate or unfavorable narratives early; the direction of change is more reliable than the exact score.

AI referral traffic is the visits a site receives when people click links inside AI answers, from referrers such as chatgpt.com, perplexity.ai, copilot.microsoft.com or gemini.google.com. Analytics tools now group much of it separately, but clicks from Google's AI Overviews and AI Mode stay mixed into ordinary Google organic traffic.

AI crawler log analysis is reading a site's server or CDN logs for the user agents of AI crawlers and fetchers, such as GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot or ClaudeBot, to see what they actually request, how often, and with what responses. It shows access, never whether content was later used or cited.

Difference-in-differences is a test design that estimates the effect of a change by comparing the before-and-after shift on treated pages with the shift on similar untreated pages over the same period. Subtracting the control group's change removes shared background drift, but the result is causal only if the groups would otherwise have moved in parallel.

Citation decay, sometimes expressed as a citation half-life, is a vendor term for the rate at which a page's citations in AI answers fall away over time when the content is left unmaintained. The direction is plausible, since answers regenerate and sources rotate, but published half-life figures are tool-specific and cannot be compared.

Citation velocity is a vendor term for how fast a page gains citations in AI answers after it is published or updated. It mirrors citation decay and carries the same caveat: there is no shared formula, so a velocity figure means something only inside the tool and the prompt panel that produced it.

Prompt tracking is the practice of running a fixed set of questions, a query panel, through chosen AI engines on a schedule and recording each answer, its mentions and its citations. The panel is the sampling frame under every AI visibility metric, so its design determines what those metrics can and cannot show.

Answer non-determinism is the tendency of AI engines to give different answers, and different citations, to the same question asked repeatedly. Generation is sampled and retrieval changes over time, so a single check proves little: visibility has to be measured as a rate over many runs of each prompt, with an error margin.

Citation gap analysis compares the sources AI engines cite for a set of target questions with a brand's own presence, to find the questions where competitors or third-party sites are cited and the brand is absent. The resulting list of gaps, by question and by cited source, becomes a plan for content and outreach work.

Share of model is a term introduced by the marketing agency Jellyfish in 2024 for how large language models perceive and present a brand within its category, and the name of its trademarked platform. Others now use it loosely for a brand's share of AI mentions. With no published formula, each share-of-model figure is proprietary.

Monitoring tools are platforms that run panels of prompts through AI engines on a schedule and report how a brand appears: mentions, citations, prominence, sentiment and competitor share. The category includes specialist startups and AI modules added to established SEO suites; their methods are proprietary, so their numbers are not directly comparable.

Unaided awareness in AI answers is whether a model names a brand spontaneously when asked a category question that does not mention it, such as "best winter jackets". a16z called it an indicator of unaided awareness in the AI era. It measures presence under given conditions, and says nothing about why the model knows the brand.

AI dark traffic is the visits influenced or generated by AI systems that analytics cannot attribute to them: clicks from assistant apps without a referrer, copied links, and pages fetched by agents on a user's behalf. Much of it lands in Direct or never reaches analytics at all, so referrer reports undercount AI's role.

Reference rate is a16z's name for how often a brand or its content is cited or used as a source in AI-generated answers, a measure the firm's 2025 essay set against click-through rate as the one that matters in generative search. It is a concept rather than a formula, usually computed as citation share or mention rate.

AIO trigger rate is the share of searches in a defined set for which Google shows an AI Overview. Pew Research measured 18 percent of searches in a US panel in March 2025, with rates rising sharply for long and question-style queries. Rates for a site's own keywords have to be tracked on its own query panel.

Prompt volume is a vendor term for an estimate of how often a question, or a topic, is asked to AI assistants: the hoped-for counterpart of keyword search volume. AI engines publish no such counts, so every figure is modeled by a vendor from its own data, and figures differ in method, coverage and granularity.

Click-through to source is the rate at which people who see an AI answer click one of its cited source links, sometimes measured for a single cited domain. The measured anchor is small: in Pew Research's 2025 study, users clicked a link inside a Google AI summary in about 1 percent of visits to pages showing one.

The citation-metric zoo is a collective label for the many metric names vendors have coined for AI search, such as citation density, citation halo, citation readiness or entity salience. Each is defined only inside its own tool's methodology, none has a cross-vendor definition or independent validation, and each should be translated into panel-based primitives before comparison.

RAG evaluation is the technical scoring of a retrieval-augmented generation system's own quality: whether retrieval returns relevant, focused context, whether answers stay faithful to that context, and whether they address the question. The RAGAS framework popularized automated, LLM-graded versions of these metrics, run by a system's builders on their own pipeline.

Absorption and fidelity describe whether an AI answer actually uses a source and represents it accurately, beyond citing it. In Martinez's 2026 survey of generative engine optimization, absorption is a source's effective contribution to an answer's facts, wording or structure, and fidelity is the extent to which attributed claims are supported and rendered faithfully.

Generative AI performance report is a Search Console view, introduced on 2026-06-03 and available to all sites since 2026-08-31, that shows how often a site's links appeared in Google's generative AI features: AI Overviews and AI Mode in Search, with a separate report for Discover. Its only metric so far is impressions.

AI Performance report is a Bing Webmaster Tools view, in public preview since 2026-02-10, that shows how often a site's pages are cited as sources in Microsoft Copilot, AI-generated summaries in Bing and select partner integrations. It reports total citations, cited pages and grounding queries, the phrases the AI used when it retrieved the site's content.

AI Assistant channel is a default channel in Google Analytics 4, added on 2026-05-13, that groups visits arriving from AI chatbots such as ChatGPT, Gemini, Copilot, Deepseek and Grok. Matching sessions get the medium 'ai-assistant'. Clicks from Google's own AI Overviews and AI Mode remain in Organic Search.

Crawl-to-refer ratio is a metric that compares how many pages a search or AI platform's bots fetch from websites with how many visitors that platform sends back through links. Cloudflare has published it on Radar since 2025-07-01; a ratio of 1,000:1 means a thousand crawl requests for each referred visit, a rough gauge of what crawling returns.

Agent Readiness score is a free check from Cloudflare, launched on 2026-04-17 at isitagentready.com, that scans a website for standards AI agents can use to find, read, access and act on it, such as robots.txt rules, Markdown negotiation, Content Signals, Web Bot Auth, Agent Skills and MCP Server Cards, then returns a score with fixes.

robots.txt is the plain-text file at a site's root where owners state which paths each crawler may fetch, standardized as the Robots Exclusion Protocol in IETF RFC 9309 (September 2022). AI companies map their opt-outs to user-agent tokens in it, but it governs crawling only, and compliance is voluntary.

X-Robots-Tag is an HTTP response header that carries the same indexing and snippet rules as the robots meta tag, such as noindex or nosnippet, for any file a server returns. It is the way to apply those rules to PDFs, images and other non-HTML resources, and it can target a single crawler by name.

Preview controls are robots directives that limit how much of a page a search engine may show, and Google documents them as the page-level way to limit use in AI Overviews and AI Mode: nosnippet, max-snippet, the data-nosnippet attribute and noindex. Since 2026, Search Console also offers a site-wide opt-out from Google's generative AI features.

llms.txt is a proposed Markdown file at a site's root, /llms.txt, that gives AI agents a description of the site and curated links to Markdown versions of its pages. Proposed by Jeremy Howard in September 2024 and revised as v2 in August 2026, it is widely published, while Google says its AI features need no such file.

llms-full.txt is a community convention that complements llms.txt: a single plain-text file containing a site's entire documentation in Markdown, so an agent can ingest everything in one request. It is not part of the llms.txt proposal, but documentation platforms such as Mintlify generate it automatically, and some files run to tens of megabytes.

ai.txt is a file format proposed by Spawning in 2023 for declaring, at a site's root, whether its text, images, audio, video and code may be used to train AI models. Spawning designed it to be checked when media is downloaded rather than when a site is crawled. Its specification page now shows only a maintenance notice.

Schema.org is the shared vocabulary of types and properties, such as Article, Organization, Product, FAQPage or DefinedTerm, that websites use to describe their content to machines, most often embedded as JSON-LD. Google requires no special schema for its AI features, but says structured data should match the text visible on the page.

DefinedTerm and DefinedTermSet are schema.org types for publishing glossaries and controlled vocabularies in machine-readable form. A DefinedTerm is a word, name, acronym or phrase with a formal definition; a DefinedTermSet is the glossary, dictionary or classification scheme that contains it. Google offers no rich result for either type.

IndexNow is an open protocol that lets a website notify participating search engines the moment a URL is added, updated or deleted, instead of waiting for a recrawl. A submission to one participating engine is shared with the others, which include Bing, Yandex, Seznam, Naver, Yep, the Internet Archive and Amazonbot. Google is not among them.

Sitemaps are files, usually XML, that list a site's URLs for search engines, optionally with the date each page last changed. Defined by the Sitemaps protocol 0.9, they help crawlers discover pages and recrawl changed ones. Google and Bing both ignore their priority and change-frequency fields and rely on accurate lastmod values.

Cloudflare Content Signals is a policy, published in September 2025, that adds machine-readable usage preferences to robots.txt through a Content-Signal line with three signals: search, ai-input and ai-train. It separates permission to appear in classic search from permission to feed AI answers or train models. Signals express preferences and do not technically block crawlers.

TDMRep, the TDM Reservation Protocol, is a W3C Community Group specification that lets rightsholders declare in machine-readable form whether text and data mining rights are reserved for their web content, and where a licensing policy can be found. It was designed as a technical answer to the opt-out in Article 4 of the EU's 2019 copyright directive.

RSL, Really Simple Licensing, is an open specification for publishing machine-readable licensing terms for content, including terms for AI training and AI answers, and for acquiring licenses at scale. RSL 1.0 was published on 2025-12-10 at Recommendation status. It adds a middle path between blocking crawlers and free access: access under stated terms.

Pay-per-crawl is Cloudflare's mechanism, introduced in private beta on 2025-07-01, that lets site owners charge AI crawlers per request: a crawler without payment intent receives HTTP 402 Payment Required with a price, and Cloudflare bills and settles as merchant of record. In July 2026 Cloudflare said it is evolving the program into Pay Per Use.

MCP, the Model Context Protocol, is an open-source standard for connecting AI applications to external systems such as data sources, tools and workflows through one common protocol. Introduced by Anthropic on 2024-11-25 and hosted since December 2025 by the Linux Foundation's Agentic AI Foundation, its current specification revision is dated 2026-07-28.

Search generative AI control is a Search Console setting, tested from 2026-06-03 and available to all sites since 2026-08-31, that lets a site owner exclude the site from Google's generative AI features (AI Overviews, AI Mode and generative AI in Discover) without affecting classic search ranking. Excluded sites receive no impressions or traffic from those features.

Bing AI Training and AI Grounding preferences are two independent publisher controls, announced on 2026-10-30, that let site owners decide separately whether Microsoft may train generative models on their content and whether it may use the content to ground AI answers. They replace the older NoArchive and NoCache signals for these uses and take effect from 2027-02-01.

Agentic Commerce Protocol (ACP) is an open standard, developed by OpenAI and Stripe and released under Apache 2.0 on 2025-09-29, that defines how AI agents, buyers and businesses complete purchases programmatically: product feeds for discovery, a checkout interface merchants implement, and delegated payment through scoped tokens. ChatGPT was its first implementation.

Universal Commerce Protocol (UCP) is an open-source standard for agentic commerce, launched by Google on 2026-01-11 with Shopify, Etsy, Wayfair, Target and Walmart, that lets AI agents discover what a business offers and complete catalog search, cart, checkout and order steps through one shared interface. Google built it to power checkout in AI Mode and the Gemini app.

WebMCP is a proposed browser API that lets a web page expose its own functions to AI agents as structured tools, such as 'search flights' or 'add to cart', so an agent can call them instead of clicking through the interface. It is a W3C community draft, edited by Microsoft and Google engineers, in origin trial in Chrome.

Agent Skills is an open format for packaging instructions, scripts and reference files that teach an AI agent a specific task. Each skill is a folder with a SKILL.md file whose name and description tell the agent when to load it. Anthropic launched it in October 2025 and donated it to the Agentic AI Foundation in October 2026.

Markdown content negotiation is serving a Markdown version of a web page to clients that request it with the HTTP header Accept: text/markdown, while browsers keep receiving HTML at the same URL. Cloudflare popularized it as Markdown for Agents on 2026-02-12, converting pages at its edge so AI agents read lighter, cleaner text.

Web Bot Auth is an emerging IETF protocol that lets bots and AI agents prove who they are by cryptographically signing their HTTP requests, using HTTP Message Signatures and a published key directory. Sites can verify the signature instead of trusting a user agent string, which is easy to spoof, or an IP list, which is hard to maintain.

AI Preferences (AIPREF) is an IETF effort to standardize how publishers state whether their content may be used for AI training, AI use and search. It defines a shared vocabulary with labels such as train-ai and search, and ways to attach those preferences through a Content-Usage HTTP header or a new robots.txt rule.

The GEO paper (Aggarwal et al.) is the 2023 study, published at KDD 2024, that coined generative engine optimization, defined ways to measure a source's visibility inside AI-written answers, released the GEO-bench benchmark, and found that adding citations, quotations and statistics raised visibility by up to 40 percent in its test setting.

GEO-bench is the benchmark released with the GEO paper: 10,000 user queries drawn from nine sources and tagged across domains and intents, each paired with the text of up to five top Google results, built to measure how visible a source is inside answers written by a generative engine. It is a common reference point for later GEO experiments.

The a16z GEO essay is "How Generative Engine Optimization (GEO) Rewrites the Rules of Search", published by venture firm Andreessen Horowitz on May 28, 2025. It helped carry GEO from research into marketing, framing AI answers as "Act II of search" and arguing that reference rates now matter alongside click-through rates.

The Pew Research AI summaries study is a July 2025 analysis of the real browsing of 900 US adults, which found that Google users clicked a traditional result on 8 percent of visits when an AI summary appeared versus 15 percent without one, and clicked a link inside the summary on just 1 percent.

The Ahrefs AI Overviews click studies are two vendor analyses of 300,000 keywords. The April 2025 study linked AI Overviews to a 34.5 percent lower click-through rate for the top-ranking page; the February 2026 re-run, over two years of data, put the gap at 58 percent. Both are correlational, per Ahrefs' own framing.

The SparkToro zero-click studies are clickstream analyses by Rand Fishkin measuring how many Google searches end without a click. The 2024 edition, on Datos panel data, found 360 open-web clicks per 1,000 US searches and 374 in the EU; the 2026 edition, on Similarweb data, found 68.01 percent of US searches ended with no click.

Google Search Central AI features documentation is Google's official guidance for site owners on AI Overviews and AI Mode: how pages become eligible, how query fan-out works, which controls limit inclusion, and how to measure it. Its 2026 guide states that optimizing for these features is still SEO and that Google Search ignores llms.txt.

OpenAI crawler documentation is the official page "Overview of OpenAI Crawlers", which describes OpenAI's four web agents, GPTBot, OAI-SearchBot, ChatGPT-User and OAI-AdsBot: what each one does, how robots.txt applies to each, where their IP ranges are published, and how long robots.txt changes take to apply.

Perplexity crawler documentation is the official "Perplexity Crawlers" page, which describes PerplexityBot, the crawler that makes sites appear and get linked in Perplexity's answers, and Perplexity-User, the fetcher that visits pages when a user asks a question, along with robots.txt behavior, published IP lists and firewall allowlisting steps.

Anthropic crawler documentation is the Claude Help Center article on Anthropic's three web agents: ClaudeBot, which collects training data; Claude-User, which fetches pages for user questions; and Claude-SearchBot, which indexes content for search quality. It states what blocking each one changes, confirms the bots honor Crawl-delay, and notes a training-preference signal recognized since October 2026.

The llms.txt proposal is Jeremy Howard's specification, first published at llmstxt.org on September 3, 2024 and revised as v2 in August 2026, for a Markdown file at a site's root that gives language models a curated summary and links to clean Markdown versions of key pages, because HTML built for people is hard for models to use.

The glossary race is the wave of GEO and AI search glossaries published by vendors, agencies and independent authors as the field grew, ranging from lists of about 45 terms to catalogs claiming more than 300. They differ widely in size, in whether their stated counts match the entries a reader can see, and in whether those entries carry dates.

The critical GEO survey (Martinez 2026) is a July 2026 arXiv preprint by Olivier Martinez that reviews 45 GEO studies from November 2023 to July 2026 and concludes the evidence is narrow: content changes can alter how already-retrieved sources are cited, but no reviewed technique shows a stable, cross-platform effect on discoverability.

An AI Search Employee is an AI agent given the goal of getting a company recommended in AI answers, the way a hire would be: it learns who the company sells to, finds what AI engines are missing about it, writes the work for approval and reports what moved. Obi, by Heysurface, Inc., uses the name for its role.

ICP-Based AI Search is an approach to AI search that starts from the ideal customer profile (ICP): the buyers who sign, their situation and their words. The questions measured come from that profile and are ranked by how close each sits to a purchase, so the work aims at recommendations that can become revenue.

Contextual Buyer Prompts are the questions asked of AI engines to measure a company's visibility, written the way a buyer in its ICP would ask them, with that buyer's words and situation. They replace prompts built from keywords, so what is measured matches what real buyers read.

A missing piece is first-hand evidence or an explanation that a company's buyers need and that AI cannot find anywhere yet, such as a page on how a product fits a five-person team. AI engines can only cite what exists, so creating missing pieces from a company's own data adds what no other source says.

Missing-Piece Content is content written to supply a missing piece: a page built from a company's own data, in its voice, that answers what buyers ask and AI cannot find elsewhere. It is shaped to be useful to visitors and easy for AI engines to read and quote, and each page is approved before it goes live.