--- language: en title: "Glossary" description: "Search, content, and AI visibility concepts explained with examples, limitations, and sources." canonical: "https://tropk.ai/en/resources/glossary" last_modified: "2026-10-12T16:03:33.223Z" --- # Glossary - [AEO](): AEO means answer engine optimization. See how content, context, and evidence help answer real questions, and how AEO relates to GEO. - [AI Mode](): AI Mode is Google’s AI search experience for questions and conversational exploration. Understand sources, fan-out, and how it differs from AI Overviews. - [AI Overviews](): AI Overviews are AI-generated summaries shown in some Google searches. Learn how they relate to sources, SEO, and brand presence analysis. - [AI SEO](): AI SEO can refer to using AI in SEO work or to visibility in AI-powered search. Learn how to distinguish goals, define deliverables, and assess quality and discoverability. - [AI slop](): AI slop refers to low-quality content produced with AI, usually in large quantities. Assessing it requires examining the material’s usefulness, evidence, and review. - [AI washing](): AI washing means presenting AI as playing a greater or different role than can be substantiated. Commercial claims must match its demonstrated operation. - [AI2Bot](): AI2Bot collects web content to train Ai2’s open models. See its published identification, traffic filtering, and verification limitations. - [AIO](): AIO means AI Search Optimization in the usage discussed here: optimization for AI search. Learn how it overlaps with AEO and its ambiguity with AI Overviews. - [Scaled content abuse](): Scaled content abuse creates many pages to manipulate rankings with little value for readers. Evaluation depends on purpose and content, not just the tool. - [Site reputation abuse](): Site reputation abuse involves third-party content published primarily to take advantage of a domain’s signals. The current policy requires evaluating context and integration. - [Accessible, selected, and extractable](): Accessible, selected, and extractable is an editorial model for separating access, observable signals of selection, and the use of excerpts, without revealing internal decisions. - [Content licensing agreement](): A content licensing agreement defines which materials one party may use, for what purposes, and under what conditions, including in AI systems. - [Hallucinated claims about a brand](): Hallucinated claims about a brand invent characteristics with an air of certainty. Checking current evidence helps distinguish errors, outdated information, and opinions. - [Agent Payments Protocol](): AP2 uses verifiable mandates to authorize checkout and payment by agents. Learn how they link to the transaction and about the limits of specification v0.2. - [Agent2Agent](): Agent2Agent standardizes communication between AI agents: capability discovery, messages, and tasks, with access defined by each implementation. - [AI agent](): An AI agent performs a task by choosing steps and tools based on feedback from its environment, within defined permissions, completion criteria, and limits. - [Agents with verifiable signatures](): Agents with verifiable signatures sign HTTP requests. Validation identifies a key and its association, but does not by itself authorize access or actions. - [Agentic Commerce Protocol](): ACP defines commercial exchanges between agents and sellers, including checkout. Implementing the protocol and participating in a commerce experience are distinct steps. - [Aggregation by page and domain](): Choose the unit used to group citations: individual URLs or domains, with explicit rules for subdomains, duplicates, and traceability. - [Prompt clustering](): Prompt clustering brings together semantically similar questions. Learn how to review clusters to track topics without erasing differences in intent and context. - [Alexa for Shopping](): Alexa for Shopping is Amazon’s shopping assistant, bringing together features from Rufus and Alexa+. The market, interface and personal context define the scope of the analysis. - [AI hallucination](): An AI hallucination is fabricated or unsupported information presented as fact. Learn how to detect errors about brands and review claims and sources. - [Amazonbot](): Amazonbot collects content to improve Amazon products and services, with possible use in training. Learn the rules and how it differs from the search agent. - [Amzn-SearchBot](): Amzn-SearchBot collects content for Amazon search experiences, such as Alexa, without collecting it for generative training. See access rules and limitations. - [Amzn-User](): Amzn-User accesses the web in response to Amazon user actions, such as questions asked to Alexa. Learn about its purpose, identification, and the limits of robots.txt. - [Profound’s Answer Engine Insights](): Answer Engine Insights is Profound’s product for analyzing tracked responses to monitor brand presence, citations, sentiment, and claims. - [Apple Intelligence](): Apple Intelligence brings together AI features built into Apple devices and apps. Each feature has device, software version, and language requirements. - [Applebot](): Applebot crawls content for Apple search experiences and other stated uses. Learn about access rules, identification, and separate controls. - [Applebot-Extended](): Applebot-Extended controls the use of content to train Apple’s models. Learn why blocking this token differs from preventing crawling or search. - [Machine learning](): Machine learning trains models on data to predict, classify, or generate content. Learn how to evaluate examples, performance, and limitations in marketing. - [AI assistant](): An AI assistant is a product that brings together an interface, models, and functions to help with tasks. Learn how context and features affect responses. - [Brand association in AI](): Brand association in AI is a characteristic linked to a company in observed responses. Learn how to check descriptions, context, and possible discrepancies. - [AI incorporation lag](): AI incorporation lag describes the observed interval between updating a source and a response reflecting the change, with test conditions recorded. - [Attribution](): Attribution distributes credit for an outcome among touchpoints in a journey. Understand its limits in AI discovery and how to distinguish observed source from causal effect. - [Self-reported attribution](): Self-reported attribution records how customers say they learned about a brand. These accounts complement data collection but do not, on their own, prove what caused a purchase. - [Brand attribute](): A brand attribute is a characteristic a company wants recognized in AI responses. See how to define evidence, conditions, and criteria. - [Content freshness](): Content freshness is how well information fits the reader’s question at a given time, assessed by relevant changes, sources, and validity conditions. - [Content updates](): A content update reviews the facts and usefulness of a page. Learn when to update, how to document changes, and why changing only the date does not demonstrate a review. - [Mention update](): A mention update corrects existing information about a brand. Learn how to document the change at the source and distinguish corrections from effects on AI responses. - [Domain authority](): Domain authority is a vendor estimate for comparing websites. Moz’s Domain Authority is not a Google factor or a guarantee of citation by AI. - [Authority in answer engines](): Authority in answer engines describes a brand’s recognition and credibility within its category. Learn about this diagnosis and its limitations in AEO. - [Borrowed authority](): Borrowed authority describes associating content with the reputation of an external publication, without automatically transferring factual credibility or rankings. - [Topical authority](): Topical authority describes credibility and coverage in a subject area. Learn how to demonstrate expertise with evidence and why page volume does not create an official score. - [Backlink](): A backlink is a link from another website to one of your pages. Learn how to assess its context, source, and destination, and why link counts do not guarantee authority. - [Vector database](): A vector database stores and queries vectors by proximity. Learn how to evaluate filters, updates, and retrieval accuracy in your application. - [Battlecard](): A battlecard is a sales enablement resource that brings together comparison criteria, responses to objections, and up-to-date evidence to guide conversations. - [Bi-encoder](): A bi-encoder represents queries and documents as separate vectors. Learn how it is used for retrieval and how to test the selected content. - [Sources block](): A sources block gathers references outside the main text. Learn why the displayed list does not prove that each statement in the answer is supported. - [Answer bubbles](): Answer bubbles describe the risk that similar summaries may narrow the options considered. Repetition in a sample does not represent all users. - [Brave Leo](): Brave Leo is the assistant built into the Brave browser for chatting and working with web pages and documents, with model options and support from Brave Search. - [Content brief](): A content brief defines the problem, reader, evidence, and deliverable for a piece. Learn how to guide research and review without reducing the work to a list of keywords. - [AI search](): AI search combines research and answer generation. Understand the role of sources, context, and platforms, and why an answer is not the same as a fixed ranking. - [Conversational search](): Conversational search links questions and answers to explore a topic in greater depth. Learn how to track context, criteria, and brand presence. - [Agentic search](): Agentic search delegates research to software that consults sources and adapts its steps. Understand the criteria, evidence, and limits when evaluating offers. - [Semantic search](): Semantic search considers meaning and context beyond exact terms. Understand how it relates to intent, synonyms, and useful content without resorting to artificial repetition. - [Zero-click search](): A zero-click search ends without a visit to another site. Learn what this behavior means for marketing and the limits of measuring discovery in AI. - [Amazon Buy for Me](): Buy for Me is a service announced by Amazon for purchasing from external brands’ websites at the user’s request, with confirmation and customer service handled by those brands. - [CCBot](): CCBot collects pages for the Common Crawl open repository. Learn about access rules, source verification, and limits on data reuse. - [CLS: Cumulative Layout Shift](): CLS measures unexpected shifts of visible elements on a page. Learn about the visual stability score and how to investigate changes during use. - [Cache](): Cache reuses stored data or responses to handle new requests. Understand its effect on your website, content updates, and performance evaluation. - [Circular sourcing](): Circular sourcing occurs when content derived from AI responses is cited again. Repetition and multiple links do not create independent evidence. - [Citation source chain](): A map of domains and publication types cited by an AI on a given topic. Learn to compare sources by platform and recognize the limits of the sample. - [Answer layer](): The answer layer presents summaries and guidance in search. Learn how to assess descriptions, sources, and differences from the list of links. - [GA4 AI Assistants channel](): The GA4 AI Assistant channel groups visits according to source and medium rules. AI Overviews and AI Mode are part of Organic Search, not this assistants channel. - [Canonical](): Canonical indicates a preferred URL among identical or very similar pages. Learn about the role of rel=canonical and how to align links, the sitemap, and redirects. - [Crawler purpose category](): Crawler purpose category describes the documented use of access, such as search, training, or user-triggered activity. It guides rules without inferring function from the name. - [ChatGPT](): ChatGPT is OpenAI’s product for conversations, analyzing materials, and performing tasks. Its search can consult the web, with its own sources and access conditions. - [ChatGPT Atlas](): ChatGPT Atlas was OpenAI’s browser with ChatGPT built in. Documentation records its discontinuation and the transition to other browsing experiences. - [ChatGPT-User](): ChatGPT-User identifies certain accesses initiated by users of ChatGPT and custom GPTs. Its role differs from automated crawling and search. - [Perplexity Instant Checkout](): Perplexity Instant Checkout lets users buy within the search experience using PayPal, under terms documented for eligible users and merchants. - [Chunking](): Chunking divides documents into sections for processing and retrieval. The size, boundaries, and overlap affect how much context each section preserves. - [Semantic chunking](): Semantic chunking aims to split text based on relationships in meaning. See how to check chunk boundaries and test the strategy in Portuguese. - [Earned citation](): An earned citation references third-party coverage of a brand. Learn how to check authorship and editorial independence before interpreting the evidence. - [Brand-owned citation](): A brand-owned citation points to content controlled by the brand being analyzed. Learn how to verify page ownership and distinguish a reference from an endorsement. - [Social source citation](): A social source citation is a reference in an AI response that points to content from a community or social network. Learn how to classify it and check its origin. - [AI citation](): An AI citation is an explicit reference to a source. Learn how to check the link, the passage it supports, and the difference between a cited source and a mentioned brand. - [Inline citation](): An inline citation is a reference alongside the text of a response. Learn how to check its destination and whether the document supports the claim associated with the link. - [Claude](): Claude is Anthropic’s AI assistant. Learn how to check web searches, references, and context without assuming every answer involves an automatic search. - [Claude-SearchBot](): Claude-SearchBot analyzes pages to improve Claude search results. Learn how to check access and distinguish indexing from training and citations. - [Claude-User](): Claude-User: page retrieval to answer requests in Claude, with a purpose and access controls distinct from Anthropic's other bots. - [ClaudeBot](): ClaudeBot collects content that may contribute to training Anthropic's models. See robots.txt controls and the limits of identifying requests. - [Cloudflare AI Crawl Control](): Cloudflare AI Crawl Control shows AI crawler activity and lets you manage access. Learn about the available actions and the limits of this visibility. - [Topic cluster](): A topic cluster organizes complementary pages around a theme and a pillar page, with links that help readers explore specific questions in greater depth. - [Co-citation](): Co-citation looks at brands or sources cited together in the same answer. Learn how to define the unit of analysis and avoid mistaking joint presence for authority. - [Prompt sample coverage](): The extent to which a set of questions represents the topics and conditions defined for an AI analysis, with gaps made explicit and no claim that it reflects the market. - [Model collapse](): Model collapse is the degradation studied in recursive training with generated data. It is not equivalent to an isolated error or to every use of synthetic data. - [Comet](): Comet is Perplexity’s browser with AI assistance. See how tab context affects an analysis and which access permissions need to be checked. - [Entity confusion](): Entity confusion occurs when a response or record mixes up identities or versions. Learn how to distinguish a name’s presence from correct attribution. - [Candidate set](): A candidate set brings together passages selected before reranking. Learn how to inspect this stage to find gaps in retrieval. - [Prompt set](): A prompt set is a selection of questions used to track AI responses. Its composition defines the scope and limits of visibility comparisons. - [Implied query](): An implied query is a question inferred from incomplete context. Check referents and constraints to avoid searching for evidence based on the wrong interpretation. - [Synthetic query](): A synthetic query is a search formulated by the system based on the request it receives. Its wording may change, so results must be evaluated and constraints preserved. - [Position-Adjusted Word Count](): A GEO research metric that weights words in sentences citing a source according to their position in the response, without measuring human attention or brand presence. - [Content Signals](): Cloudflare framework for declaring content-use preferences for search, AI input, and training, separate from technical access controls. - [Business context](): Business context brings together the offering, audience, market, and constraints to guide marketing. Learn how to distinguish facts, hypotheses, and information that needs review. - [Atomic content](): Atomic content organizes units that cover one subject and retain their own meaning. Reuse requires explicit context, conditions, and dependencies. - [Machine-readable content](): Machine-readable content presents text and structure in a representation that a program can parse, without guaranteeing access, understanding, or citation. - [Co-occurrence](): Co-occurrence identifies terms that appear together in a text unit. Learn how to choose the scope and investigate brand associations without inferring authority. - [Copilot Search](): Copilot Search is the AI answer experience in Bing. Understand the difference between sources used in the synthesis and other links shown in the results. - [AI Copilot](): An AI copilot, in the context of agents, prepares proposals and submits actions for human decision. Assess permissions, review, and execution in the specific workflow. - [Core Web Vitals](): Core Web Vitals measure loading, response to interactions, and visual stability. Understand LCP, INP, and CLS, field data, and thresholds for SEO decisions. - [Core update](): A core update is a broad change to Google's ranking systems. Changes around a rollout call for diagnosis and do not prove a penalty or an impact on AI. - [Custom corpus](): A custom corpus is a curated collection of searchable documents. Selection, updates, and permissions determine which evidence a response can retrieve. - [Training data cutoff](): Training data cutoff is the stated time reference for a model’s data. The date does not guarantee complete coverage of earlier facts. - [Effective knowledge cutoff](): Effective knowledge cutoff estimates the version of a source that is most temporally aligned with the model. It may differ from the stated cutoff and vary by resource. - [Crawl budget](): Crawl budget describes a site's crawling capacity and demand. Learn when it deserves attention and why it isn't a priority for every SEO project. - [Crawl-delay](): Crawl-delay is a robots.txt extension for requesting a slower crawl rate. Support depends on the operator; Google does not interpret this field. - [Branded search growth](): Branded search growth compares searches for a brand by name across defined periods and datasets. An increase may have several causes and does not prove an exclusive AI effect. - [Cross-encoder](): A cross-encoder evaluates a query and passage together to score relevance. Learn how it is used in reranking, what to test, and its time and input limits. - [Training data](): Training data consists of examples used to adjust a model’s parameters. Their selection, origin, and composition influence the behavior learned. - [Structured data](): Structured data describes information in an explicit format for machines. Learn how it is used on pages, how it relates to Schema.org, and what to validate before publishing. - [Synthetic data](): Synthetic data are artificially generated examples for training, testing, or analysis. Learn how to assess their usefulness, origin, and limits of representativeness. - [DeepSeek](): DeepSeek offers chat and API access to its models. Identifying the service, interface, and version avoids comparing responses obtained under different conditions. - [Sentiment drift](): Sentiment drift tracks persistent changes in the tone of AI responses, using a comparable baseline to distinguish trends from changes in data collection. - [Entity disambiguation](): Entity disambiguation distinguishes people, brands, or products with similar names using identifiers, attributes, and references to the correct identity. - [Knowledge staleness](): Knowledge staleness occurs when answers retain facts that are no longer valid. Checking dates and sources helps distinguish outdated information from fabrication. - [Knowledge distillation](): Knowledge distillation trains a student model to imitate a teacher. Learn how to assess cost, speed, and quality loss by task. - [Bot directory](): Bot directories organize identifiers and categories for automated services. Use their records alongside operator sources and observed traffic. - [Naming disputes in AI search](): Naming disputes in AI search involve terms such as GEO, AEO, and LLMO. See how to compare scopes and deliverables without treating acronyms as standardized methods. - [Cited domain](): A cited domain groups URLs referenced in AI responses. Learn how to define the unit of analysis without confusing pages, subdomains, and visits. - [Inline domain](): Inline domain identifies the domain of a reference inserted into the text of an AI response. How the link is displayed does not replace identifying the cited page. - [Duck.ai](): Duck.ai is DuckDuckGo’s chat interface with third-party models. The selected model and the distinction from search answers define the scope of the evaluation. - [E-E-A-T](): E-E-A-T brings together experience, expertise, authoritativeness, and trustworthiness. Learn how it is used to evaluate content and why it is not an official ranking score. - [Embeddings](): Embeddings represent texts as vectors to compare relationships in meaning. Learn about their role in search and what to watch for when evaluating similar results. - [Prompt engineering](): Prompt engineering formulates and tests instructions for a task. Learn how to use success criteria and examples without mistaking improvement for a guarantee. - [Relevance engineering](): Relevance engineering connects content with discovery in search and AI. Learn about iPullRank’s framework, practical decisions, and the limits of this professional approach. - [Marketing engineer](): A marketing engineer combines marketing context with the development of systems, integrations, and experiences. The scope should be defined by deliverables. - [Entity](): An entity is something identifiable, such as a brand, person, or product. See how names, context, and consistent data help distinguish your company from others with the same name. - [Recommendation poisoning](): Recommendation poisoning attempts to bias choices through manipulated content. Third-party comparisons and rankings require clear criteria, sources, and interests. - [Retrieval poisoning](): Retrieval poisoning introduces content to distort answers when retrieved. Checking provenance and claims helps investigate the source of the deviation. - [Prompt space](): Prompt space brings together the possible ways to ask about a topic. It helps shape samples that include relevant intents, audiences, and conditions. - [Entity establishment](): Entity establishment organizes names, identifiers, and verifiable attributes to present a coherent identity across pages and public sources. - [Evidence on schema and AI citations](): Evidence on schema and citations requires distinguishing association, intervention, and the stage being evaluated. Valid structure does not demonstrate a causal increase in AI presence. - [Prompt execution](): A prompt execution is each submission of a question to an AI platform, with the input, configuration, and result recorded to support metrics. - [Content extractability](): Content extractability is the ability to extract passages with their context from processed material, without confusing extraction with citation in AI. - [FCP: First Contentful Paint](): FCP measures when the first content appears on screen after navigation. Learn about the 1.8-second benchmark and how it differs from LCP and full page load. - [Profound’s FactCheck](): FactCheck is a Profound feature that checks claims about a brand against a knowledge base and shows the citations associated with inaccuracies. - [Featured snippet](): A featured snippet highlights a search result excerpt in Google Search. Systems choose whether to display it; markup and text limits do not guarantee selection. - [Citation faithfulness](): Citation faithfulness checks whether a source supports what an answer claims. Learn to check conditions, numbers, and limits before using the information. - [Content fidelity](): Content fidelity compares an answer with the meaning and data of the source used. A correct citation does not prove that figures and conditions were preserved. - [Brand fidelity](): Brand fidelity assesses whether responses accurately describe an organization and its offerings. Mention, favorable tone, and accuracy are different evaluations. - [Fine-tuning](): Fine-tuning adapts a previously trained model to a task or domain. Learn when to test this technique and how to evaluate its results. - [AI answer source](): An AI answer source is material retrieved to formulate a response. Learn how to distinguish a search, a visible citation, and factual support. - [Primary source](): A primary source provides first-hand information, such as documentation, research, or a record. Learn how to select evidence, check scope, and distinguish facts from commercial claims. - [Answer-first format](): Answer-first format presents the direct answer before the explanation. Organize conditions, evidence, and details without hiding essential qualifications. - [AI attribute strength](): AI attribute strength is a favorable characteristic that appears accurately in the analyzed responses. It requires confirming the attribute and defining the scope. - [Mention frequency](): Count of brand occurrences in the analyzed responses, including repetitions. Understand the units and the difference between frequency and presence rate. - [GEO](): GEO is the work of improving how a brand and its content appear in AI-generated answers. Understand its relationship with SEO, sources, and measurement. - [GPTBot](): GPTBot is OpenAI’s crawler for content that may contribute to model training. Learn how to distinguish this collection from access for ChatGPT search. - [Information gain](): Information gain is the useful contribution content adds to what the reader already knows, with evidence or explanations relevant to their task. - [Gemini](): Gemini names Google's assistant and a family of models. Evaluation should distinguish the app, the API, and the features enabled for each account. - [Gemini Notebook](): Gemini Notebook is Google's research tool based on sources selected by the user. Learn about its scope, the name change, and how to review responses. - [Google Analytics](): Google Analytics measures events on websites and apps. Learn how to use GA4 to assess user journeys, check data collection, and distinguish visits from business outcomes. - [Google Search Console](): Google Search Console reports presence and performance in Google Search. Understand clicks, indexing, and URL inspection without confusing diagnosis with guaranteed rankings. - [Google-Agent](): Google-Agent identifies visits by agents hosted by Google that browse and act at the user's request. Learn how to verify their origin and define permissions. - [Google-Extended](): Google-Extended controls the use of content for training and grounding Gemini systems. It is a robots.txt token separate from Search controls. - [Google-GeminiNotebook](): Google-GeminiNotebook identifies the fetcher for URLs provided as sources in Gemini Notebook. Understand user-requested access and the previous token. - [GoogleOther](): GoogleOther is Google's generic crawler for public content. Learn about its identification, permissions, and the limits of interpreting its visits. - [Googlebot](): Googlebot crawls pages for Google Search. Learn how to verify its identity and distinguish access, robots.txt blocking, and indexing control. - [Knowledge graph](): A knowledge graph represents entities and relationships using a defined structure and vocabulary. Learn how to check identity, connections, and data sources. - [Grok](): Grok is SpaceXAI’s AI assistant with web and X search. Learn how to verify social sources and distinguish an answer from a reputation metric. - [Grounding](): Grounding connects an AI response to reference information. Learn how to check sources and claims, and why an answer with links still needs review. - [HTML: the web’s markup language](): HTML defines the structure and meaning of a page’s content. Learn about elements, links, and headings, and what to check beyond a site’s visual appearance. - [HTTP 402](): HTTP 402 is the Payment Required code, reserved in RFC 9110. Learn about its uses in automated billing and why the response does not confirm payment. - [Headings: HTML section titles](): Headings are section titles marked with h1 to h6 in HTML. Learn how to organize content hierarchy without confusing visual size, SEO titles, and structure. - [Generative AI](): Generative AI produces text, images, and other content based on learned patterns. Learn about its use in editorial work and what to check before publication. - [Multimodal AI](): Multimodal AI works with more than one type of data, such as text and images. See how to test whether an analysis really depends on visual information. - [INP: Interaction to Next Paint](): INP assesses the visual response to clicks, taps, and keyboard input during a visit. Learn how to investigate interaction delays and interpret the 200-millisecond benchmark. - [AI search impressions](): AI search impressions may be instrumented events or volume-weighted exposure estimates; the data source and method should be explicit. - [Indexing](): Indexing is the analysis and organization of pages in a search index. Learn why an accessible URL may be left out of the index and how to investigate. - [Inference](): Inference is the execution of a trained model to produce a prediction or response. Learn what to look for when evaluating generation, latency, and production use. - [Review-site influence](): Review-site influence describes how this material appears in AI responses. Analysis requires checking sources, claims, and study limitations. - [Artificial intelligence](): Artificial intelligence brings together systems that perform tasks such as forecasting, language processing, and decision-making. Learn what to assess in marketing applications and understand their limitations. - [Artificial general intelligence](): AGI refers to artificial intelligence with broad capabilities across different tasks. Learn about the competing criteria and how to assess claims of generality. - [Search intent](): Search intent is the need behind a question. Learn to distinguish learning, comparison, and action to choose content and interpret AI responses. - [Latent intent](): Latent intent is the need a question leaves implicit. Learn how to investigate the context without treating a hypothesis as certainty about the audience. - [JSON-LD](): JSON-LD is a format for linked data used in page markup. See how it relates to Schema.org and how to check syntax and editorial consistency. - [Context window](): A context window is the token space available for a model’s inputs and response. Its limit affects the documents, history, and instructions used. - [Keyword stuffing](): Keyword stuffing uses terms or numbers out of context to try to manipulate rankings. There is no universal density that can replace clarity and usefulness. - [LCP: Largest Contentful Paint](): LCP measures when the largest eligible content appears in the page’s visible area. Learn about loading, the 2.5-second benchmark, and field and lab analysis. - [LLM](): An LLM is a language model used to interpret and generate text. Learn the difference between a model and an assistant, and how it affects brand monitoring. - [LLM as a judge](): LLM-as-a-judge uses a language model to evaluate responses against defined criteria. Learn about biases and calibration with human review. - [LLMO](): LLMO is the optimization of how brands are represented in responses from language models. Learn how to define scope, review sources, and distinguish this work from training. - [Attribution gap](): The attribution gap separates potential brand touchpoints from what data collection and the model can attribute. Presence in AI does not equal a proven sale. - [Citation gap](): A citation gap is a snapshot in which competitors receive references and your domain does not. Identifying one depends on the questions, platforms, and period analyzed. - [Generated query gap](): Generated query gap compares the searches in an observed fan-out with the answers available in a content library, using a defined set and evaluation criterion. - [Content gap](): A content gap is a need that the existing content does not adequately address. Learn how to identify missing information, update pages, and avoid creating redundant articles. - [Late chunking](): Late chunking creates embeddings for chunks after processing a larger context. The technique aims to preserve references between parts of the same document. - [Agent readability](): Agent readability describes the clarity of content and interfaces in the representations an agent uses to recognize information, relationships, and actions. - [Data cleaning](): Documented handling of errors, formats, and duplicates before analysis, while preserving original data, exclusion rules, and the basis for metrics. - [Internal links](): Internal links connect pages on the same site. Learn how to guide navigation and discovery with published destinations, clear anchor text, and relationships that help readers. - [Brand attribute list](): A brand attribute list organizes verifiable characteristics to guide questions and AI analysis. Learn how to set priorities and references. - [Brand placement in text](): Brand placement in text indicates where a brand mention begins in a response. Word, paragraph, or percentage range requires an explicit measurement rule. - [Author markup](): Author markup identifies authors in structured data, with names and references that match the page. Declaring a person does not prove their expertise. - [Attention mechanism](): The attention mechanism combines input representations with weights calculated by the model. Learn how to interpret its limitations when evaluating AI responses. - [Answer engine](): An answer engine provides a direct explanation in response to a question. Learn how to assess sources, alternatives, and differences between generative engines. - [Generative engine](): A generative engine combines information retrieval and answer generation. Learn about the role of sources and attribution, and the limits of observing third-party systems. - [Non-parametric memory](): Non-parametric memory is information kept outside a model’s parameters and accessed through retrieval, such as a document index queried in RAG. - [Parametric memory](): Parametric memory is knowledge represented in a model’s learned parameters. Updating a page does not directly modify these parameters. - [Authentic brand mention](): Authentic brand mention is a Profound label for accurate information in a source already cited by AI. See how to check the context and limits of the reference. - [AI mention](): An AI mention is the occurrence of a brand in the text of a response. Learn to distinguish mentions, recommendations, and citations, and to check names and namesakes. - [Reciprocal mention](): A reciprocal mention involves coordinated references between brands. Learn how to check the agreement, the editorial context, and the difference between a mention and a link exchange. - [Meta AI](): Meta AI is Meta’s assistant, available in its apps, on the web, and in a standalone app. Analysis needs to identify the surface and feature used. - [Microsoft Copilot](): Microsoft Copilot offers AI assistance across Microsoft experiences. Check the app and context before interpreting a response or task. - [MistralAI-User](): MistralAI-User identifies page accesses requested by Vibe users. Learn its purpose, how to verify its origin, and what site controls apply. - [Mixture of Experts](): Mixture of Experts uses routing to activate parts of a network. Learn why total and active parameters help evaluate different models. - [Model Context Protocol](): MCP is an open communication protocol between AI applications and external services, which can expose data, prompts, and tools with defined capabilities and access. - [SEO team coordination model](): The SEO team coordination model organizes briefs, responsibilities, and deliverables across teams to carry out search priorities with evidence and review. - [Frontier model](): A frontier model refers to capabilities near the state of the art at a given time. Learn why the label requires criteria, a date, and task evaluation. - [Foundation model](): A foundation model is a base trained on broad data and adaptable to various tasks. Learn to identify dependencies and evaluate the application that uses it. - [Prompt monitoring](): Prompt monitoring repeats queries under recorded conditions. It helps track responses, citations, and facts without mistaking the sample for the entire audience. - [NLWeb](): NLWeb brings together open protocols and tools for querying websites in natural language, with structured responses and MCP integration in the reference implementation. - [AI browser](): An AI browser incorporates browsing assistance, such as search and tab analysis. Actions on websites depend on the product’s features, access, and permissions. - [Agentic browsing](): Agentic browsing occurs when an AI navigates and interacts with websites to complete a user’s task, subject to permissions and confirmation of results. - [Wikipedia notability](): Wikipedia notability assesses whether a subject may warrant a Wikipedia article. Independent coverage matters; popularity and promotional material are not enough. - [New mention in an external source](): In Profound's glossary, a new mention in an external source means adding a brand to an article already cited by AI. Learn how to compare versions and distinguish a mention from a backlink. - [Non-determinism in LLMs](): Non-determinism in LLMs is the variation in outputs under seemingly identical inputs. Learn how to compare responses and distinguish variation from factual errors. - [OAI-AdsBot](): OAI-AdsBot visits pages submitted as ads in ChatGPT to assess safety. OpenAI may also use their content to determine display relevance. - [OAI-SearchBot](): OAI-SearchBot crawls pages for ChatGPT search features. Learn about access controls and why allowing the bot does not guarantee a citation. - [AI Attribute Objection](): An AI attribute objection is a recurring caveat about a brand in the responses analyzed. It helps distinguish actual shortcomings from outdated descriptions. - [AI attribute opportunity](): An AI attribute opportunity identifies a relevant gap in brand representation within an analyzed segment, without assuming demand or return. - [Generative features opt-out](): A generative features opt-out lets you exclude content from AI experiences defined by the operator, with a scope distinct from data collection and training. - [Chunk optimization](): Chunk optimization adjusts segments used for information retrieval. Evaluate boundaries, context, and evidence for each task rather than adopting a universal size. - [Payment per inference](): Payment per inference links compensation to the use of content in an AI output. Learn how it differs from retrieval and what evidence is needed for billing. - [Pay-per-crawl](): Pay-per-crawl compensates content owners for automated access to content. See the billing unit, the Pay Per Crawl example, and what a crawl does not prove. - [Knowledge panel](): A knowledge panel brings together information about an entity in Google Search, combining sources and correction suggestions without guaranteeing creation or updates. - [Parasite SEO](): Parasite SEO describes publishing on a third-party domain to leverage its search ranking. The risk depends on the purpose and the applicable policy. - [Share of sources](): Share of sources describes how much of the sources in answers belongs to a domain. Occurrences, unique URLs, and domains require distinct counting bases. - [Answer engine market share](): Answer engine market share compares platforms within a defined dataset. The source, period, unit and coverage determine the scope of the data. - [Citation share](): Citation share measures the proportion of references attributed to a page or domain. How to interpret it depends on the unit, the total counted, and repetitions. - [Brand perception in AI](): Characteristics and categories associated with a brand in AI-generated responses. Learn how to assess this representation without confusing it with consumer opinion. - [Perplexity](): Perplexity combines search and answers with sources. Learn about the scope of Perplexity Search and how to evaluate citations without confusing presence with recommendation. - [Perplexity-User](): Perplexity-User retrieves pages in response to user actions. See the caveat about robots.txt and how to distinguish access from use in the response. - [PerplexityBot](): PerplexityBot is Perplexity’s search crawler. Learn its purpose, how to check permissions, and why access does not prove citation. - [Persona](): Persona defines the buyer profile and context in tracked prompts, allowing responses to be compared across audiences with different needs. - [Original research](): Original research produces evidence using its own methods and data. Learn how to publish useful findings, explain the sample, and avoid generalizations in marketing and GEO. - [Inverted pyramid](): The inverted pyramid organizes text from the most important information to supporting details. Priorities depend on the reader’s needs and the context. - [Brand position in the answer](): Brand position in the answer is an order among brands under an explicit rule. Interpreting it requires distinguishing textual sequence, lists, and aggregated rankings. - [AI Usage Preferences](): AI usage preferences express how content may be processed. Learn about the IETF’s work, its limits, and how it differs from blocking access. - [Presence in recommendation lists](): Presence in recommendation lists is the strategy of seeking relevant inclusion in third-party selections, taking criteria, evidence, and context into account. - [Wikipedia presence](): Wikipedia presence means having an article about the organization, subject to editorial criteria and independent sources, without guaranteeing citations in AI systems. - [Brand prominence](): Brand prominence describes how prominently a brand features in a response, considering order, placement, and the space devoted to it. Combining these dimensions requires documented criteria. - [Depth of mention](): Depth of mention examines how information about a brand is developed. Length, explained attributes, and accuracy help distinguish description from repetition. - [Prompt](): A prompt is input sent to an AI system. Learn its role in monitoring, how to record context, and why a selected question does not represent the entire market. - [Branded prompt](): A branded prompt includes a brand or product in the question. Understand how it is used to check reputation, descriptions, and comparisons without confusing it with spontaneous discovery. - [Compound-job prompt](): A prompt that combines a purchasing need with audience, use, and constraint requirements, allowing recommendations to be evaluated in a defined situation. - [Commercial prompt](): A commercial prompt expresses interest in choosing an offer or provider. The assessment depends on purchasing conditions and does not prove a transaction. - [Comparative prompt](): A comparative prompt asks for differences between alternatives based on criteria. It helps evaluate offers and check whether versions, conditions, and evidence are equivalent. - [Discovery prompt](): A discovery prompt asks for options in a category without naming brands, making it possible to track the alternatives presented for a specific need. - [Market perception prompt](): A market perception prompt asks AI for criteria to evaluate a category without naming brands, helping investigate how responses frame it. - [Factual accuracy prompt](): A question used to check AI claims about a product against appropriate documentation, distinguishing factual errors, omissions, and unverifiable information. - [Sentiment prompt](): A prompt requesting an evaluation of a brand by attribute, including advantages and caveats, to analyze the response’s tone without inferring consumer opinion. - [Validation prompt](): A validation prompt checks whether an AI response recognizes a specific capability of a brand by comparing what it says with current documentation. - [Generative prompt](): A generative prompt requests an output, such as a draft or script. Learn how to evaluate the result without confusing creation, research, and brand recommendations. - [Informational prompt](): An informational prompt seeks data or an understanding of a topic. Its evaluation considers accuracy, context, and depth, without assuming purchase intent. - [Prompt injection](): Prompt injection attempts to change a model’s behavior through inputs treated as instructions. Access limits and output checks reduce the impact. - [Indirect prompt injection](): Indirect prompt injection arrives through webpages, files, or other external sources. Retrieved content must remain separate from the application’s instructions and permissions. - [Navigational prompt](): A navigational prompt seeks a specific destination, such as a portal or document. Verification checks whether the provided address meets the stated goal. - [Unbranded prompt](): An unbranded prompt describes a need without naming a brand. See how to use these questions to study discovery and comparison based on audience criteria. - [Competitor prompt](): A request that compares named brands in a use case and asks for a recommendation, helping examine the criteria and reasoning behind the AI response. - [Transactional prompt](): A transactional prompt requests a concrete action. Distinguishing between a request, execution, and confirmation prevents counting an assistant’s response as a completed conversion. - [Buyer journey prompts](): Buyer journey prompts organize questions about discovery, evaluation, and purchase. The map guides content without assuming a fixed path for each person. - [Agent readiness](): Agent readiness assesses whether a website or service enables defined tasks to be completed with access, understanding, controls, and verifiable evidence. - [Alternatives page](): An alternatives page presents substitutes for a familiar solution and explains under what conditions each option meets the need behind the search. - [Comparison page](): A comparison page evaluates identified options using the same criteria and conditions, with evidence and limitations that help readers choose based on their needs. - [Statistics page](): A statistics page brings together data on a topic with clear sources, time periods, units, and bases, allowing readers to verify the numbers before reusing them. - [Pillar page](): A pillar page presents an overview of a topic and links its main subtopics to in-depth content, offering a useful answer on its own. - [Query fan-out](): Query fan-out expands a question into related searches. Learn its role in AI responses and how to analyze observed queries without treating them as measured demand. - [RAG](): RAG combines information retrieval and response generation. Learn why current sources can be used in AI searches and what limitations remain. - [RLHF](): RLHF uses human evaluations to guide model training. Learn about the role of preferences and the limitations of evaluating AI responses. - [Citation ranking](): Citation ranking orders pages or domains by their share of collected references, with the period, counting basis, and tie handling made explicit. - [Visibility ranking](): Visibility ranking orders brands by score within a defined scope. The comparison set, measurement basis, and ties determine how to interpret the position. - [Crawlability for AI](): Crawlability for AI checks whether an identified agent can reach a URL and receive its content, taking purpose, permissions, and the actual response into account. - [Crawler](): A crawler is a program that visits pages to collect content. Its purpose and the server’s responses guide the assessment of access for search or AI. - [Mixed-use crawler](): A mixed-use crawler serves more than one documented purpose. Learn how to review uses and controls without assuming that each request reveals its ultimate destination. - [Crawling](): Crawling is automated access to pages by a bot. Learn how to check for blocks and server responses, and distinguish access, indexing, and citation. - [Crawl-to-referral ratio](): The ratio of crawler requests to referral requests for a platform, calculated using the same data set, period and log criteria. - [Bot-to-human visit ratio](): The bot-to-human ratio compares visits classified within the same scope. Requests, sessions, and people are different units and require compatible datasets. - [Really Simple Licensing](): Really Simple Licensing expresses content licenses in XML. Learn about the scope of the RSL 1.0 specification and the difference between declaring and enforcing a license. - [Citation recall](): Citation recall measures the proportion of verifiable claims in a response that are fully supported by the cited sources. Learn how to define your baseline. - [AI recommendation](): An AI recommendation occurs when a response suggests a brand for a specific need. Learn how to assess context, criteria, fit, and the difference between a recommendation and a simple mention. - [Information retrieval](): Information retrieval selects documents or passages relevant to a query. Learn how to check the retrieved material before evaluating the answer. - [Passage retrieval](): Passage retrieval selects relevant excerpts for a question. Learn how to evaluate the context retrieved before judging an AI response. - [Referrer](): Referrer indicates a navigation source when available. Policies may omit or reduce this data; its absence does not prove a direct visit. - [Reindexing](): Reindexing is the reprocessing of a page in the index after changes. Compare the published version with the indexed data before considering the update complete. - [Generative AI performance report](): The Search Console generative AI report shows impressions in AI Overviews and AI Mode. Aggregations and exports require care when comparing data. - [Impression-only report](): An impression-only report shows recorded appearances without measuring subsequent actions. The counting rule must accompany the total and any comparisons. - [Relationship between rankings and citations](): The relationship between rankings and citations compares search position with use as a source in AI. The association depends on the query, surface, period, and method observed. - [Digital PR for AI](): Digital PR for AI connects editorial coverage with tracking sources in responses, without guaranteeing a brand mention, selection or citation. - [Long-tail relevance](): Long-tail relevance is the fit for questions with specific criteria. Learn how to document use cases and restrictions without promising AI recommendations. - [JavaScript rendering](): JavaScript rendering produces content from a page’s scripts. See what to check so search engines can read titles, text, and links. - [Server-side rendering](): Server-side rendering generates HTML before delivering it to the client. Checking it distinguishes received content, hydration, and subsequent JavaScript processing. - [Reranking](): Reranking reorders results retrieved for a query. Learn how to compare the lists, assess relevance, and recognize the limits of this search stage. - [AI response](): An AI response is the output a system presents for a prompt. Defining its boundaries makes it possible to measure mentions and citations without confusing responses with sessions. - [Profound’s SAGE](): Profound’s SAGE organizes an AEO cycle into Setup, Analyze, Generate, and Engineer. Learn about its stages, deliverables, and limitations as a vendor’s methodology. - [SEO](): SEO improves how pages are discovered and understood by search engines. Learn its fundamentals and how it relates to traditional search and AI-generated answers. - [Off-page SEO](): Off-page SEO concerns a company’s presence and references outside its website. Learn how to assess links, mentions, and external activities with evidence and limitations. - [On-page SEO](): On-page SEO covers improvements to the content and elements of a website’s own pages. Understand its scope, what to check, and how it differs from off-page SEO. - [Brand SERP](): A brand SERP is the results page for a search using a company’s name. Learn how to check identity, descriptions, and sources under recorded conditions. - [SGE](): SGE was Google's experiment with generative answers in Search. Its historical context helps interpret studies conducted before AI Overviews. - [Entity salience](): Entity salience describes how central an entity is within a text or under a defined method. It is not equivalent to brand popularity, frequency, or recommendation. - [Schema FAQPage](): Schema FAQPage represents pages of frequently asked questions. The type remains in Schema.org, but FAQ rich results no longer appear in Google Search. - [Schema Organization](): Schema Organization describes an organization using Schema.org properties. Identity and contact details must be accurate; valid markup does not guarantee results. - [Schema Product](): Schema Product describes products or services with structured attributes and related offers. Accurate, current data matters more than filling in unsupported fields. - [Schema.org](): Schema.org is a vocabulary of types and properties for describing content. Learn how it relates to JSON-LD, structured data, and validation, without promises of citations. - [Aggregated sentiment score](): Aggregated sentiment score summarizes tone classifications. The scale, unit, weights, and basis must be known to compare results or time periods. - [Factual accuracy score](): A factual accuracy score compares verifiable claims against defined references. The report should show errors and inconclusive cases as well as the percentage. - [Visibility score](): A brand presence indicator whose formula depends on the provider. Understand Profound’s denominator and why different scores require explicit methodologies. - [Sentiment in AI](): Sentiment in AI classifies the tone of passages about a brand or attribute. Interpreting it requires context and a rubric; it does not measure consumers’ actual opinions. - [Citation hijacking](): Attributing content or data to another source in AI responses. Learn how to check authorship, references, and limitations before concluding where the misattribution originated. - [Adversarial text sequence](): An adversarial text sequence is inserted into a source to influence a model’s recommendation. Controlled research does not prove an effect across all search engines. - [MCP server](): An MCP server provides tools, resources, and prompts to compatible clients. Its usefulness depends on the functions it exposes and the permissions granted. - [AI Share of Voice](): AI Share of Voice compares a brand’s presence with that of competitors in the sample. Understand the denominator and why the metric does not represent market share. - [Cosine similarity](): Cosine similarity compares vectors by direction. Learn about its role in semantic search and why a score does not prove relevance or truth. - [Third-party signals](): Third-party signals are information about a company published outside its own channels. Learn how to assess context, source, and how they differ from validation. - [Sitemap](): A sitemap informs search engines about URLs and updates. Learn how to select pages, keep dates accurate, and check the file without treating it as a guarantee of indexing. - [Agent Skills](): Agent Skills bring together reusable instructions and resources for specific tasks. The Agent Skills format organizes this material into folders containing SKILL.md. - [Slopsquatting](): Slopsquatting exploits names invented by models to register malicious packages. A package’s presence in a repository does not prove that a dependency is trustworthy. - [Competitive overlap](): Competitive overlap counts responses in which two brands coexist under a defined rule. Their joint presence requires interpretation and does not prove preference. - [GEO spam](): GEO spam describes the abusive creation or alteration of content to manipulate AI responses. Scale and the use of AI alone do not demonstrate abuse. - [HTTP Status](): HTTP status indicates the result of a request. Learn how 200 responses, redirects, and errors affect page access, and why technical success does not guarantee indexing. - [Response synthesis](): Response synthesis turns a question and passages into an explanation. Learn how to verify the support for each claim and preserve the sources’ caveats. - [TBT: Total Blocking Time](): TBT adds up the time by which long tasks exceed the threshold for blocking the browser’s main thread. Learn about lab diagnosis and how it differs from INP. - [TTFB: Time to First Byte](): TTFB measures the wait until the first byte of the response. Understand network, redirects, server, and cache without confusing this measure with content visible on screen. - [Zero-click search rate](): Zero-click search rate measures the share of searches or tasks with no outbound click, according to the protocol. The unit and counted destination must accompany any percentage. - [Citation rate](): Citation rate measures how often the specified domain or URL is referenced. See how to calculate it, handle responses without sources, and compare periods using the same baseline. - [AI traffic conversion rate](): The AI traffic conversion rate relates actions to identified visits within a stated scope. Comparisons require the same action, unit, and collection window. - [AI presence rate](): Percentage of valid answers that include the brand at least once. See the formula, topic breakdown, and considerations for datasets and collection failures. - [First-mention rate](): First-mention rate counts responses in which the brand is named before others, with a valid denominator and explicit reading rules for interpreting the percentage. - [Temperature in LLMs](): Temperature in LLMs adjusts randomness during generation. Learn how to compare text variations and why the parameter does not guarantee factual accuracy. - [Anchor text](): Anchor text is the visible part of a link. Learn how clear labels guide people and search engines, and avoid generic anchors or promises the destination can’t fulfill. - [Token](): A token is a unit that represents part of the text for a language model. The count depends on the tokenizer and is not equivalent to the number of words. - [Discontinued crawler tokens](): Discontinued tokens are identifiers whose support has ended, according to a statement from the operator. Check the date, the replacement, and the limits of this evidence. - [TollBit](): TollBit is a platform for analyzing bot traffic and managing licensed access to content by AI systems, with controls and billing defined by the publisher. - [Transformer](): Transformer is a neural network architecture based on attention mechanisms. Learn what it does and why it does not prove a tool’s quality. - [AI referral traffic](): AI referral traffic brings together visits identified as coming from assistants. See how to check sources, pages, and conversions without confusing visits with mentions. - [Traffic with no identifiable source](): Traffic with no identifiable source consists of visits whose origin cannot be determined from the available signals. A missing referrer does not prove AI influence. - [Depth topic](): A monitoring segment dedicated to a specific opportunity, grouping questions with a coherent audience, criteria, and needs to guide analysis. - [Universal Commerce Protocol](): UCP organizes commerce capabilities between platforms and sellers. Discovery, checkout, and payment authorization have distinct roles and checks. - [User agent](): A user agent identifies the client software; in HTTP, the header declares information about it. It helps analyze traffic but does not, by itself, authenticate the visitor. - [Third-party validation](): Third-party validation is the confirmation of a claim by an external source. Learn how to assess the evidence, independence, and limits of that confirmation. - [Incumbent advantage](): Incumbent advantage describes a preference for established brands in certain recommendations. Evidence and context matter; the effect is not universal. - [Variability in retrieved sources](): Variability in retrieved sources compares documents obtained across repeated runs of the same question, subject to limits on access to the logs. - [Response variability](): Variability is the difference between responses to equivalent questions. Learn why repetition, sample size, and collection conditions matter in AI monitoring. - [Citation velocity](): Citation velocity tracks changes in references over time. Interpreting the measure requires defining the count, time period, and comparable response base. - [Source visibility](): How often a URL or domain appears as a source in analyzed AI responses, based on a defined dataset, period, and counting rule. - [AI visibility](): AI visibility indicates a brand’s presence in observed responses. Learn how to define the sample and distinguish presence, citation, recommendation, and commercial impact. - [Position bias](): Position bias describes how the location of information in the context can change how a model uses it. Learn about the test and the limits of this evidence. - [Answer volatility](): Answer volatility measures fluctuations in visibility across comparable data collections and helps distinguish an observed change from usual variation. - [Prompt volume](): Prompt volume describes how often requests occur within a defined scope. Check the source, period, and unit to distinguish audience demand from test runs. - [Web Bot Auth](): Web Bot Auth uses HTTP signatures to identify automated traffic. See the protocol status, key verification, and the limits of authentication. - [WebMCP](): WebMCP proposes structured tools on web pages for browser agents. Learn about its experimental status and how it differs from a remote MCP server. - [Wikidata](): Wikidata organizes entities, properties, and references in a collaborative, multilingual database. An item is not the same as a Wikipedia article and does not guarantee use by AI. - [llms-full.txt](): A supplementary file that brings together expanded documentation content for AI tools, distinct from the llms.txt index and with no guarantee of being read or affecting rankings. - [llms.txt](): llms.txt is a proposed file containing context and links for agents. Learn its purpose and why it does not replace robots.txt, a sitemap, or the fundamentals of SEO. - [max-snippet](): A directive that limits the search text snippet by character count, with effects documented by Google on content input to its AI features. - [meta-externalagent](): meta-externalagent identifies Meta’s crawler for training and direct indexing. Learn how to check specific rules without confusing it with other agents. - [meta-externalfetcher](): meta-externalfetcher fetches links requested by users and supports Meta’s agentic capabilities. Learn about the possible exception to robots.txt rules. - [meta-webindexer](): meta-webindexer crawls pages to improve Meta AI search. Learn about access controls and why visits do not prove citations in responses. - [noai](): A preference signal against AI uses, adopted in contexts such as Fab, whose meaning and effect depend on the platform and do not guarantee technical blocking. - [noarchive](): A directive historically associated with page caching, now ignored by Google and still documented by Bing with specific effects for Chat, Copilot, and training. - [noindex](): noindex instructs search engines not to index a page. Learn how to check the directive and why blocking crawling can prevent Google from reading it. - [nosnippet](): A preview-control directive recognized by Google and Bing, with distinct effects in each search engine on snippets and content use in AI features. - [robots.txt](): robots.txt provides access rules for crawlers. Understand its limitations, how it differs from noindex, and how to distinguish search bots from training bots. - [sameAs](): sameAs links an item described with Schema.org to pages that unambiguously indicate its identity, without making every external link a valid equivalence. - [utm\_source=chatgpt.com](): utm_source=chatgpt.com is added to ChatGPT Search referral links to analyze visits. The tag declares a source but does not authenticate provenance. - [Search index](): A search index is a searchable structure that organizes information about documents. Learn how it relates to crawling, retrieval, and presence in search results. --- [All pages in Markdown]()