--- language: en title: "Luiz Gardelin | Author at Tropk" description: "Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands improve their presence in AI responses." canonical: "https://tropk.ai/en/team/luiz-gardelin" last_modified: "2026-10-12T16:03:31.513Z" --- # Luiz Gardelin [Back to the blog]() Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands understand and improve their presence in AI responses. Marketing · SEO · GEO/AEO · Content strategy [LinkedIn]() ## Articles by this author ### [What Is Visibility Score in AI Responses]() Learn about brand presence in AI responses, how to distinguish mentions from citations, and what it takes to make a Visibility Score comparable across platforms and time periods. 2026-07-01T02:34:23.000Z ### [Topical Authority: What It Is and How to Build It to Appear in AI Responses]() Learn about topical authority, organize pillar pages and clusters, connect content, and track whether your topic coverage helps people find your brand in AI responses. 2026-07-01T02:34:36.000Z ### [What Is Share of Voice in AI Responses]() Learn how to calculate AI Share of Voice, distinguish share from visibility rate, and compare mentions, recommendations, and citations using the same set of questions and brands. 2026-07-01T02:34:20.000Z ### [Schema Markup for AI Visibility: The 7 Types That Matter and How to Implement Each]() Explore seven schema families, see valid JSON-LD examples, and learn how to mark up identity, articles, FAQs, and products with real data—without promising citations or special results. 2026-07-01T02:34:33.000Z ### [robots.txt for AI: which bots to allow and how to configure it]() Separate search, training, and user-requested access when configuring robots.txt for OpenAI, Perplexity, Claude, and Google. Includes file templates and validation. 2026-04-14T12:07:36.000Z ### [What Is SEO (Search Engine Optimization): The Complete Guide]() Learn what SEO is, how crawling, indexing, and ranking work, the technical and editorial pillars, and how to track traditional search alongside AI answers. 2026-04-13T18:57:39.000Z ### [What Is GEO (Generative Engine Optimization): Complete 2026 Guide]() Learn about GEO, source retrieval, RAG, and query fan-out. See how to organize content, authority, and tracking to increase the chances of appearing in AI answers. 2026-04-06T17:42:32.000Z ### [llms.txt: what it is, how to implement it, and what it changes about AI visibility]() Learn about the current llms.txt proposal, how it differs from robots.txt and sitemaps, its uses in documentation, and the limits of evidence on AI citations. 2026-07-01T02:34:14.000Z ### [Technical guide: how to optimize your website and content to be cited by AI]() Implement clear content, structured data, robots.txt, and sitemaps with code examples. Learn what each setting does and which promises to avoid. 2026-04-13T16:51:46.000Z ### [GEO vs. AEO vs. SEO: What’s the difference?]() Compare SEO, AEO, and GEO by their goals, answer formats, and metrics. Understand how these practices overlap and how to prioritize them for brand discovery. 2026-04-13T15:17:47.000Z ### [ChatGPT Ads: how advertising works on ChatGPT and what it changes in marketing]() ChatGPT Ads is now operational. Learn how advertising relates to responses, which official features are available, and how to plan campaigns and measurement. 2026-04-13T18:22:45.000Z ### [What Is AEO (Answer Engine Optimization): The Complete Guide]() Learn what AEO is, how to structure direct answers, review technical access and sources, and measure mentions, citations, and business results without relying on clicks alone. 2026-04-08T17:33:23.000Z ### [How to choose questions to monitor your brand in AI responses]() Choose discovery, comparison, and purchase questions that help you understand how your brand appears in AI responses. See examples and a worksheet for organizing intent, criteria, and what to observe. 2026-10-03T08:18:35.421Z ## Guides by this author - [GEO and AEO Guide]() - [GEO and AEO Tools]() ## Resources by this author - [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. - [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. - [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. - [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. - [Profound’s Answer Engine Insights](): Answer Engine Insights is Profound’s product for analyzing tracked responses to monitor brand presence, citations, sentiment, and claims. - [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. - [Battlecard](): A battlecard is a sales enablement resource that brings together comparison criteria, responses to objections, and up-to-date evidence to guide conversations. - [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. - [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. - [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. - [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. - [E-E-A-T Checklist](): 47 checks covering experience, expertise, authoritativeness and trust. Review authorship, sources, evidence, company information and editorial practices using observable criteria. - [Strategic SEO Checklist](): 40 decisions to connect audiences, search intent, content assets and content priorities. Organize evidence, owners and the next improvement cycle. - [Perplexity Instant Checkout](): Perplexity Instant Checkout lets users buy within the search experience using PayPal, under terms documented for eligible users and merchants. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [Where do sources cited by AI come from? How they differ by sector and engine](): An analysis of Profound’s study of 11.84 billion citations. See why company, editorial, and community sources need to be evaluated in context. - [From Retrieved Page to Cited Source: How to Read AirOps’ Commercial Study](): AirOps analyzes 217,508 pages retrieved for commercial queries. Learn how to interpret structure, tables, and statistics without turning associations into formulas. - [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. - [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. - [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. - [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. - [Recommendation poisoning](): Recommendation poisoning attempts to bias choices through manipulated content. Third-party comparisons and rankings require clear criteria, sources, and interests. - [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. - [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. - [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. - [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. - [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. - [Information gain](): Information gain is the useful contribution content adds to what the reader already knows, with evidence or explanations relevant to their task. - [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. - [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. - [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. - [AI search impressions](): AI search impressions may be instrumented events or volume-weighted exposure estimates; the data source and method should be explicit. - [Review-site influence](): Review-site influence describes how this material appears in AI responses. Analysis requires checking sources, claims, and study limitations. - [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. - [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. - [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. - [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. - [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. - [AI mentions and website visits: what the Profound study allows us to conclude](): More than two million conversations help investigate visits after brand mentions. Understand the comparison, its causal limits, and what the absence of a referral does not prove. - [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. - [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. - [Prompt monitoring](): Prompt monitoring repeats queries under recorded conditions. It helps track responses, citations, and facts without mistaking the sample for the entire audience. - [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. - [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. - [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. - [Asking in Portuguese doesn’t limit searches to that language: a Peec AI analysis](): Peec AI investigated the use of English searches after questions in other languages. Learn about the denominators, limitations, and how to test relevance for Brazil. - [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. - [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. - [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. - [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. - [Prompt for writing a comparison article](): Compare options across the same dimensions, cite sources for each attribute, use equivalent scopes, and tie recommendations to usage scenarios. - [Prompt for writing an alternatives article](): Identify real substitutes for a reference solution, with eligibility criteria, reasons to switch, and migration conditions. - [Prompt for writing a Q&A article](): Group related questions and write self-contained answers with conditions and sources, without claiming frequency that has not been measured. - [Prompt for writing a list article](): Create a curated selection with inclusion criteria, ordering, consistent item profiles, and usage guidance, without padding it to meet an artificial count. - [Prompt for writing an informational article](): Turn a specific question into a clear explanation with a definition, example, limitations, and sources for SEO, AEO, and GEO. - [Prompt for writing a case study](): Reconstruct an authorized real-world experience with context, actions, comparable results, and limitations, without treating association as causation. - [Prompt for writing a guide or pillar page](): Organize a broad topic into decisions, examples, and deeper dives, with a scope map that prevents overlap with the cluster. - [Prompt for writing a tutorial or how-to](): Create instructions with requirements, verifiable steps, and troubleshooting, preserving the version and evidence for the procedure. - [Prompt for writing a research study or benchmark](): Present an analysis with a research question, sample, method, denominators, and limitations, using real data and verifiable calculations. - [Prompt for writing about a trend or update](): Explain a verifiable change with dates, the previous state, affected audiences and conditional actions, without presenting an announcement as availability. - [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. - [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. - [How Long Does an AI Citation Last? A Look at the Profound Study](): The study tracks the declining share of pages cited by seven engines. Learn about the 11 days, the methodology, and how to plan reviews without chasing fluctuations. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. - [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. --- [All pages in Markdown]()