--- language: en title: "What Is GEO (Generative Engine Optimization)? Complete Guide 2026" description: "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." canonical: "https://tropk.ai/en/blog/what-is-geo-generative-engine-optimization-complete-2026-guide" last_modified: "2026-10-12T16:05:27.740Z" --- # What Is GEO (Generative Engine Optimization)? Complete Guide 2026 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. [Luiz Gardelin]() Published on 2026-04-06T17:42:32.000Z Categorias: GEO/AEO [**Generative Engine Optimization](/en/blog/what-is-geo-generative-engine-optimization-complete-2026-guide) ([GEO](/en/resources/glossary/geo))** is the practice of optimizing content to be cited in answers from generative AIs such as [ChatGPT](/en/resources/glossary/chatgpt), [Gemini](/en/resources/glossary/gemini), [Perplexity](/en/resources/glossary/perplexity), and [Claude](/en/resources/glossary/claude). This practice follows the growth of search for synthesized answers: ChatGPT and other assistants let people research and compare sources, while [AI Overviews and AI Mode are part of Google Search](https://developers.google.com/search/docs/appearance/ai-features). But appearing isn’t enough. In an AirOps study of [548,534 pages analyzed from 15,000 prompts](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt), **85% of pages retrieved by ChatGPT never make it into the final answer**. Most enter the funnel and die there. GEO exists to increase the chance that your brand moves beyond [retrieval](/en/resources/glossary/information-retrieval) and becomes a [citation](/en/resources/glossary/ai-citation). This didn’t emerge from hype. The term was formalized in the academic paper [“GEO: Generative Engine Optimization”](https://arxiv.org/pdf/2311.09735), presented at KDD 2024, with its own benchmark, 10,000 queries, and visibility gains of up to 40% in the experimental setting. Results vary by domain and method and are not a promise for every website or current engine. So yes, GEO can now be treated as a discipline. Not a fad. ## Key points - GEO aims to make content discoverable, understandable, and citable in generative responses; retrieval does not guarantee citation. - SEO continues to support discovery, while AI tracking monitors questions, sources, and brand descriptions. - Query fan-out expands searches beyond the original question and calls for coverage of related questions. - Clear definitions, self-contained sections, sources, and contextualized data help people and systems use content. - Results vary by engine, question, and time period; measure observed presence and business outcomes separately. ## What Is Generative Engine Optimization [**Generative Engine](/en/resources/glossary/generative-engine) Optimization (GEO)** is an optimization discipline focused on increasing the chance that content will be retrieved, understood, and cited by response systems based on language models. The goal is not just to rank. It is to become part of the narrative AI builds about a brand, product, category, or question. ### The formal definition and the paper that coined the term **GEO** was formalized by researchers from Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi in the paper [“GEO: Generative Engine Optimization”](https://arxiv.org/pdf/2311.09735), first published in November 2023 and presented at KDD 2024 in Barcelona. The study created **GEO-bench**, a benchmark with [10,000 queries and 9 datasets](https://arxiv.org/pdf/2311.09735), testing optimization techniques in engines such as ChatGPT and Bing Chat. In the paper’s experimental setting, some techniques increased visibility by up to **40%**, depending on the metric and domain assessed. This result is not a forecast for current engines or for every website. **Cite Sources**, **Statistics Addition** and **Quotation Addition** had relative gains of around [30% to 40% in position-adjusted word count](https://arxiv.org/html/2311.09735v3), with different results for the subjective impression metric. Don’t compare percentages without identifying the metric. And here’s the first unexpected finding: [**keyword stuffing](/en/resources/glossary/keyword-stuffing) reduces visibility**. In the paper, the effect was around [\\-10%](https://arxiv.org/pdf/2311.09735). Anyone who treats AI like 2008 [SEO](/en/resources/glossary/seo) will lose ground. ### From link search to answer search Search has become less like a list and more like a conversation. The user asks, AI synthesizes, selects sources, omits others, and delivers a ready-made answer. In many cases, there’s no click at all. This shift is already reflected in the numbers. Google still processes a huge volume of searches every day, but ready-made answers have gained prominence. ChatGPT has become a starting point for research, comparison, evaluation, and recommendations. And Google [AI Overviews](/en/resources/glossary/ai-overviews) has expanded this behavior within the search engine itself, with generative answers to informational and commercial queries. The effect on traffic is real. Ahrefs measured an average drop of [35.2% in clicks](https://ahrefs.com/blog/how-to-rank-in-ai-overviews/) when an AI Overview is present. And a figure cited by Valor Econômico indicates that [93% of AI search interactions end without a click](https://valor.globo.com/patrocinado/pressworks/noticia/2026/03/23/generative-engine-optimization-ganha-forca-e-muda-a-logica-de-visibilidade-digital-aponta-especialista-1.ghtml). The answer has become the destination. ### What GEO isn’t **GEO isn’t a new name for SEO.** GEO uses some of SEO’s foundations, but measures a different type of result and competes on a different surface. **GEO isn’t an [LLM](/en/resources/glossary/llm) ranking hack.** Models don’t work like a classic SERP with ten visible links in a stable order. **GEO isn’t about repeating keywords for machines.**Academic data shows the opposite. But there’s an important point: GEO doesn’t replace SEO. In the AirOps study, pages ranked first on Google were [3.5 times more likely to be cited](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt) than pages outside the top 20. And [55.8% of cited pages](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt) ranked in the top 20 for at least one related query. A strong organic presence remains the foundation. ## GEO, SEO, and AEO: what’s different **SEO, GEO, and [AEO](/en/blog/what-is-aeo-answer-engine-optimization-the-complete-guide) are closely related disciplines, but have different goals, mechanisms, and metrics.** SEO aims for search engine rankings. [AEO](/en/resources/glossary/aeo) aims to provide direct answers in the SERP. GEO aims for citations in answers generated by LLMs. ### Comparison table: SEO, GEO, and AEO | Criterion | SEO | GEO | AEO | | --- | --- | --- | --- | | Goal | Rank in search results | Be cited in AI answers | Be the direct answer | | Result | List of links in the SERP | Answer synthesized by an LLM | Featured snippet or direct answer block | | Primary metric | Position, CTR, sessions | Generative share of voice, citations | Snippet rate, presence in answer boxes | | Interaction | Click on the link | Reading the answer, often without a click | Answer without a click | | Competition | SERP results and formats | Variable number of sources per answer | Answer blocks and other formats | | Common factors | Keywords, backlinks, speed, intent | Semantic clarity, authority, structure, data | Answer structure, concision, authority | | Technical dynamics | Search algorithm | LLM + retrieval + synthesis | Search algorithm | Table adapted from [Aggarwal et al. (2024)](https://arxiv.org/pdf/2311.09735) and the synthesis by [Wikipedia in Brazilian Portuguese](https://pt.wikipedia.org/wiki/Generative_engine_optimization), with editorial adjustments. ### The transition: from crawled, indexed, ranked to chunked, retrieved, synthesized The most useful metaphor here is simple: previously, content was crawled, indexed, and ranked. Now it needs to be broken into understandable parts, retrieved at the right moment, and synthesized into an answer the AI can stand behind. This explains why so much technically “good” content disappears from answers. It may be crawlable and indexable, but it isn’t ready to be extracted, compared, and cited. And the market is already keeping up with this shift. According to data compiled by Valor Econômico, AI already accounts for between [12% and 15% of the global search market in 2025](https://valor.globo.com/patrocinado/pressworks/noticia/2026/03/23/generative-engine-optimization-ganha-forca-e-muda-a-logica-de-visibilidade-digital-aponta-especialista-1.ghtml), with projections that it will exceed 60% by 2030. You don’t have to buy into every market forecast to see the basics: the way people discover information has changed. ### Backlinks versus brand mentions **In SEO, links help with discovery and play a role in systems for analyzing relevance. In search-based answers, mentions in trusted sources can provide context about a brand.** This doesn’t eliminate links, nor does it mean that every mention is immediately learned by the model or turned into a preference. A brand frequently mentioned in lists, news articles, reviews, and trusted databases gains semantic depth. These documents may be retrieved to associate attributes and contexts with the [entity](/en/resources/glossary/entity). Usage depends on the system and query, with no guarantee of a recommendation. That’s why media relations, rankings, comparisons, communities, and original content are back at the center of the strategy. There’s less glamour in this than many people would like. But it works. ## How generative search works under the hood **Generative search combines a language model, information retrieval, and text synthesis.** Content needs to be understandable at each of these stages. Not just to the [crawler](/en/resources/glossary/crawler). ### How LLMs process information **LLM** means **Large Language Model**, or large language model. It doesn’t “know” facts the way a database does. It predicts the next word based on statistical patterns learned from large volumes of text. That’s why a pure LLM can sound convincing and still be wrong. This is the problem of [hallucination](/en/resources/glossary/ai-hallucination). To reduce this risk, systems combine the model with [grounding](/en/resources/glossary/grounding) mechanisms, which anchor the answer in external sources retrieved in real time. This detail changes everything for content. AI doesn’t read your article the way a human does. It breaks it into fragments, represents it semantically, compares it with the question, and selects useful passages. ### RAG: how AI searches for sources in real time [**RAG (Retrieval-Augmented Generation)**](/en/resources/glossary/rag) is the technique that lets a system search external sources before generating an answer. In a common implementation, it works in three stages: the content is divided into **chunks**, these chunks are converted into vector representations, and then the user’s query is compared with this database to retrieve semantically similar passages. The final stage is the most visible and the least controllable: the model uses the retrieved chunks to put together the answer. Not everything retrieved will be cited. In AirOps’ ChatGPT sample, 85% of retrieved pages were left out; this proportion does not automatically apply to every RAG system. This explains why understanding RAG is so helpful for GEO. You’re not writing just to be found. You’re writing to be a usable component in a composed answer. ### Structured content and chunkability **Chunkability** is how easily content can be broken into useful passages for retrieval and citation. Chunkable text reduces ambiguity, keeps each passage self-contained, and makes the answer clear in the very first sentence. In practice, this calls for: - short paragraphs, each with one central idea; - [headings](/en/resources/glossary/headings-html-section-titles) that precisely signal the topic; - clear definitions up front; - less reliance on vague pronouns like “this” and “that”; - specific, localized examples. When each section stands on its own, AI faces less friction when extracting information. When the text relies too heavily on prior context, retrieval gets worse. ### Query Fan-Out: the sub-searches no one sees **Fan-out queries** are sub-searches automatically generated by AI from the user’s main question. They are one of the most underrated GEO mechanisms. In the AirOps study, [89.6% of queries generated two or more fan-out queries](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt). And here’s the finding that undermines many strategies based solely on keyword tools: [95% of these fan-out queries have zero search volume](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt) in traditional tools. In other words, ChatGPT might start with “what is generative engine optimization” and internally explore paths such as “how to appear in ChatGPT,” “AI optimization,” “difference between GEO and SEO,” and “[AI visibility](/en/resources/glossary/ai-visibility) metrics.” If your content only covers the main keyword superficially, it won’t keep up with the search expansion. Even more striking: [32.9% of citations came only from fan-out queries](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt), not the original [prompt](/en/resources/glossary/prompt). This means a significant share of AI visibility comes from searches the user never sees. ## How each AI engine decides what to cite **Generative systems can retrieve and cite sources in different ways.** First Page Sage provides estimates based on commercial queries, but its percentages are not official weights published by OpenAI, Google, Perplexity, or Anthropic. Also note the version, whether search is enabled, the language, and personalization. For those who want to explore this in more detail, it’s worth reading our in-depth analysis of how each LLM decides what to cite. ### ChatGPT: authoritative lists dominate According to First Page Sage, in a study of [11,128 commercial queries across 4 chatbots](https://firstpagesage.com/seo-blog/generative-engine-optimization-geo-explanation/), the company attributed **41%** to **authoritative lists** on **ChatGPT**, according to its methodology. Next came **awards and accreditations**, with **18%**, **online reviews**, with **16%**, **client examples**, with **14%**, and **social sentiment**, with **11%**. This pattern makes sense when you look at commercial answers. ChatGPT tends to prefer recognized compilations, editorial rankings, and sources that have already done the screening work. ### Gemini: Google authority carries more weight On **Gemini**, the logic shifts a little. For general queries, First Page Sage reports **49% for lists**, **23% for authority signals from Google**, **15% for awards** and **13% for reviews**. In local searches, the weight of **local reviews** rises to **38%**, followed by **lists** at **29%**, **general reviews** at **19%** and **local profile signals** at **14%**. This brings local GEO closer to traditional digital reputation management. Those who ignore reviews, local listings, and information consistency tend to disappear faster from Gemini. ### Perplexity: the simplest and most predictable The **Perplexity** may be the most readable. For general queries, the study indicates **64% weight for lists**, **31% for reviews** and **5% for awards**. In local searches, **local reviews** reach **39%**, lists account for **34%** and general reviews account for **27%**. In other words, Perplexity tends to rely heavily on content that is already well organized in lists and comparisons. It favors clear editorial structure. ### Claude: traditional databases dominate For **Claude**, First Page Sage's table assigns **68%** to **traditional databases and directories**, **19%** to **awards** and **13%** to **customer examples**. The updated page also describes Claude's use of web search. The table is the consultancy's interpretation, not an official description of exclusive access to static sources. The findings suggest looking into public documentation and the sources actually cited for each question. A company's performance cannot be inferred solely from how long it has been around or from a table of weights. ### Comparative table of First Page Sage estimates | Engine | Main signal in the analysis | Consultancy estimates | Practical takeaway | | --- | --- | --- | --- | | ChatGPT | Authoritative lists | Lists 41%, awards 18%, reviews 16%, customers 14%, social 11% | Editorial rankings and external validation matter a lot | | Gemini | Lists + Google authority | Lists 49%, Google authority 23%, awards 15%, reviews 13% | Organic reputation and Google signals carry more weight | | Perplexity | Lists | Lists 64%, reviews 31%, awards 5% | A more direct engine, based on compilation and reviews | | Claude | Traditional databases | Databases 68%, awards 19%, customers 13% | Established and institutional sources come out ahead | Source: [First Page Sage](https://firstpagesage.com/seo-blog/generative-engine-optimization-geo-explanation/). ## What the data says about AI visibility **AI visibility is filtered through a tough funnel, with few winners and many left out.** Looking only at [indexing](/en/resources/glossary/indexing) or traffic means missing the most important part. ### 85% of pages are ignored In the AirOps study of [548,534 pages and 15,000 prompts](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt), **85% of pages retrieved by ChatGPT were not cited**. That means simply being available is not enough. The AI has to consider a page useful enough to include in its final answer. The same study found that pages with **50% or more title-query overlap** had a **20.1% [citation rate](/en/resources/glossary/citation-rate)**, compared with **9,3%** for pages with less than 10% overlap, a lift of [2.2 times](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt). Titles still matter, but for a different reason: they help AI recognize semantic relevance with greater confidence. ### Writing signals AI rewards—and those it penalizes Growth Memo analyzed [1.2 million ChatGPT responses, 5,000 pages, and 7 verticals](https://www.growth-memo.com/p/the-science-of-what-ai-actually-rewards). Across the study's seven verticals, the strongest shared signal was: **declarative language in the introduction**, with an average lift of **14%**. The winning format is direct. “GEO is the practice of optimizing content for AI citations.” Not “GEO can be understood as an approach that might help.” State verified facts clearly and preserve uncertainty when the evidence does not support a conclusion; the observed association does not justify overstating claims. Another counterintuitive finding: **3 to 4 headings performed worse than zero headings across all verticals** analyzed. The ideal varies by content type. In CRM and SaaS, for example, pages with **20 to 49 headings** had a higher citation rate. So the mistake isn't using headings. It's using too few, distributing them poorly, and giving them no clear purpose. Another useful detail: entities of type **DATE** and **NUMBER** showed positive associations across the verticals analyzed. Entities of type **PRICE** showed a negative association in this subset; this does not mean omitting prices needed for a buyer's decision is advisable. And pages focused heavily on entities already validated in a [knowledge graph](/en/resources/glossary/knowledge-graph) performed worse, at around **0.81x**. Sometimes a page “about the famous topic” performs worse than a page “that answers the specific question.” ### Market data: the scale of the change Adoption data helps convey urgency without hype. According to Valor Econômico, based on sources such as ALM Corp, Gracker.ai, Search Engine Land, and McKinsey, AI already accounts for between [12% and 15% of the global search market in 2025](https://valor.globo.com/patrocinado/pressworks/noticia/2026/03/23/generative-engine-optimization-ganha-forca-e-muda-a-logica-de-visibilidade-digital-aponta-especialista-1.ghtml), combined use of search engines and AI has grown by [26% since 2023](https://searchengineland.com/ai-assistants-global-search-engine-volume-study-471118), and about [50% of consumers already use AI as their primary source of information](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search). In DataReportal's global report, ChatGPT has [62.5% market share among consumer AI tools in 2026](https://datareportal.com/reports/digital-2026-one-billion-people-using-ai). This helps explain why so much GEO content starts with ChatGPT. Not because it is the only relevant engine, but because it still accounts for a lot of user behavior. ### The 9 strategies tested by researchers **GEO strategies were academically tested in the researchers' benchmark.** Measures vary by setting, metric, and domain; they are not a universal table of expected results. | Strategy | Average effect | Practical takeaway | | --- | --- | --- | | Cite Sources | Lift in the experiment; varies by metric | Cite reliable sources in the text | | Statistics Addition | Lift in the experiment; varies by metric | Use verifiable numbers with context | | Quotation Addition | Lift in the experiment; varies by metric | Include short, properly attributed quotes | | Fluency Optimization | Moderate | Write fluently and precisely | | Unique Words | Moderate | Use distinctive vocabulary without exaggeration | | Technical Terms | Moderate | Define terms correctly | | Authoritative Tone | Moderate | Be assertive when the data supports it | | Easy-to-understand | Moderate | Clarity still matters | | Keyword Stuffing | -10% | Artificial repetition gets in the way | Source: [Princeton paper presented at KDD 2024](https://arxiv.org/pdf/2311.09735). ## GEO in Brazil **GEO in Brazil is already a present-day issue, not a topic imported from some abstract future.** The use of AI to find information has grown rapidly. Brands are lagging further behind than people are. ### Brazil on the generative search map The adoption figures below were cited in the original April 2026 edition, based on third-party analyses. Visits, interactions, weekly users, and API usage are distinct measures; they do not amount to a single current market-share figure. Brazil is the [3rd globally in weekly ChatGPT usage](https://intelexia.com.br/chatgpt-no-brasil-top-3-em-uso-e-como-impulsionar-sua-empresa/), behind the United States and India, and sees around [140 million interactions per day](https://intelexia.com.br/chatgpt-no-brasil-top-3-em-uso-e-como-impulsionar-sua-empresa/). The country also ranks [2nd for the number of professionals using the OpenAI API](https://intelexia.com.br/chatgpt-no-brasil-top-3-em-uso-e-como-impulsionar-sua-empresa/). In parallel, Conversion reports that [54% of Brazil’s population used generative AI in 2025](https://www.conversion.com.br/blog/generative-engine-optimization/). This figure matters because it changes the premise. We are not talking about a technology limited to technical teams. The public is already asking questions. The question is: does your brand appear as a trusted answer in Brazilian Portuguese? ### The impact already happening The change is already having a measurable effect. CNDL reported a [27% drop in Amazon Brazil’s organic traffic between January and July 2025](https://cndl.org.br/varejosa/inteligencia-artificial-e-o-fim-dos-cliques-queda-de-trafego-organico-expoe-nova-era-dos-buscadores-no-e-commerce/). The figure stands out not only for its size, but also for what it represents. If it is happening to Amazon, it is not a side issue. Data cited by Valor also shows that small and medium-sized businesses may see a drop of [up to 60% in traffic from traditional search engines](https://valor.globo.com/patrocinado/pressworks/noticia/2026/03/23/generative-engine-optimization-ganha-forca-e-muda-a-logica-de-visibilidade-digital-aponta-especialista-1.ghtml) in this new landscape, while AI is becoming the first research source for half of consumers. Then there is the less discussed side: conversion. Wicomm cites a conversion rate of [16% for B2B traffic from ChatGPT](https://www.wicomm.com.br/o-que-e-o-generative-engine-optimization). This figure should be treated with caution until the [primary source](/en/resources/glossary/primary-source) is confirmed, but it points to something relevant: less volume does not necessarily mean less value. In many cases, the remaining visits are more qualified. To assess the value of this traffic, compare identified visits and conversions in your own operation, using the same event and time period. ## GEO ranking factors **GEO visibility factors combine authority, clarity, structure, and trust signals.** None of this is entirely new. What has changed is the relative weight. ### E-E-A-T in the context of GEO [**E-E-A-T**](/en/resources/glossary/e-e-a-t) stands for **Experience, Expertise, Authoritativeness, Trustworthiness**. [Google uses these concepts to assess quality](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) and clarifies that E-E-A-T is not a specific ranking factor. Experience, authorship, and evidence help assess a source, but they do not form a universal score confirmed for all AI engines. The first “E,” for **Experience**, becomes more prominent when content includes original data, field learnings, real comparisons, concrete examples, and an editorial point of view. AI tends to reward content that seems grounded in practice rather than just compiled from other sources. ### Topical authority and content clusters [**Topical authority**](/en/resources/glossary/topical-authority) is a domain’s ability to cover a topic in depth, coherently, and with interconnected content. This matters because AI does not assess only one page in isolation. It detects patterns. Clusters still matter. A pillar article on GEO, with satellite articles on RAG, AEO, fan-out, AI Overviews, measurement, and industry-specific cases, helps build a more consistent semantic territory. And here is a point many teams overlook: AI is more likely to cite sources that demonstrate knowledge of a topic across multiple pieces, not just one optimized URL. ### Schema Markup for AI engines [**Schema Markup**](/en/blog/schema-markup-for-ai-visibility-the-7-types-that-matter-and-how-to-implement-each) describes entities and content in a structured way and should match what people see. Use **Article**, **FAQPage**, **HowTo** and **Speakable** only when appropriate for the content and purpose. [Google does not require special schema for AI Overviews or AI Mode](https://developers.google.com/search/docs/appearance/ai-features). FAQPage remains in the [Schema.org vocabulary](https://schema.org/FAQPage), but [Google ended FAQ rich results in May 2026](https://developers.google.com/search/updates#deprecating-the-faq-rich-result-feature). Also, check technical access according to the service. [OpenAI documents OAI-SearchBot for search](https://developers.openai.com/api/docs/bots), [GPTBot](/en/resources/glossary/gptbot) for content that may be used in training, and [ChatGPT-User](/en/resources/glossary/chatgpt-user) for actions initiated by users. For Google Search, AI Overviews and [AI Mode](/en/resources/glossary/ai-mode) use [Googlebot](/en/resources/glossary/googlebot) controls. Also review [robots.txt](/en/resources/glossary/robots-txt), your CDN, and hosting for relevant crawlers such as [PerplexityBot](/en/resources/glossary/perplexitybot), without confusing search with training. ### What LinkedIn has to do with GEO LinkedIn has firmly entered the AI visibility landscape. Semrush showed that the platform is the **#2 most cited by AI systems**, appearing in [11% of the responses analyzed](https://www.semrush.com/blog/linkedin-ai-visibility-study/). The semantic similarity between LinkedIn content and [AI responses](/en/resources/glossary/ai-response) ranged from **0.57 to 0.60**. This changes how we think about distribution. Leadership posts, technical opinions, research summaries, and insightful comments on LinkedIn are not just social branding. They can become source material for citations. For many Brazilian brands, this is an underused GEO channel. ## How to implement GEO in practice **Implementing generative engine optimization requires adjusting content, structure, data, distribution, and measurement for AI-based search surfaces.** The work starts with the content, but does not end there. ### Content AI can extract Each relevant section should start with a direct answer. Growth Memo measured a [14% lift](https://www.growth-memo.com/p/the-science-of-what-ai-actually-rewards) for declarative introductions, and AirOps showed that alignment between title and query increases the citation rate by [2.2 times](https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt). In practice: 1. **Answer in the first sentence** with clear structure. 2. **Define the technical term** at first mention. 3. **Use short paragraphs**, each with a single idea. 4. **Include verifiable data** in each relevant section. 5. **Give the number human context**, so it does not become mere decoration. ### Heading structure and semantic hierarchy Headings need to work as semantic entry points. Visual organization alone is not enough. They should help AI understand exactly which question that section answers. Some rules work well: - use H2 for central questions or topics; - use H3 for specific angles; - avoid skipping levels; - maintain lexical consistency with real queries; - avoid overly generic headings. And the important data point still stands: [3 to 4 headings performed worse than zero](https://www.growth-memo.com/p/the-science-of-what-ai-actually-rewards) across all verticals analyzed by Growth Memo. This is an association in the studied sample, not a required number of headings. Structure content according to the topic and readability; [Google does not define an ideal number of headings](https://developers.google.com/search/docs/fundamentals/seo-starter-guide). ### Data and sources as citation anchors **Verifiable data and explicit sources help people check information and showed gains in the GEO benchmark.** The [GEO paper](https://arxiv.org/html/2311.09735v3) found gains from Statistics Addition, Cite Sources, and Quotation Addition in its experimental setting. The effect varied by metric and domain, with no guarantee for all sites. So the recommended approach is simple: - include data when needed to support the answer; - link directly to the exact source page; - prioritize primary research, large-scale reports, and official data; - provide context for dates and numbers. ### Topical authority and content clusters Create a network of pages that reinforce one another. One post can explain generative engine optimization; a set of complementary pages can explore questions in greater depth and connect evidence, without guaranteeing citations. This network may include: - a definition of GEO; - the difference between GEO, SEO, and AEO; - a guide to RAG; - an article about fan-out queries; - [GEO tools](/en/resources/geo-and-aeo-tools); - use cases for e-commerce, B2B, and local businesses; - monitoring AI visibility. ### GEO tools To analyze a brand's presence in AI, [Tropk.ai](/en) brings together questions, answers, and sources and guides content audits. Confirm the environments and tracking conditions available within the scope of your operation. Other tools can complement these, depending on how they are used: - **Ahrefs** and **Semrush**: traditional SEO, AI Overviews, and SERP context. - **Profound** and **Otterly.ai**: tracking AI citations. - **AirOps**: analysis of fan-out queries and retrieval behavior. - The [Portuguese (Brazil) Wikipedia article on generative engine optimization](https://pt.wikipedia.org/wiki/Generative_engine_optimization) still has a useful list of tools frequently mentioned in the market. ### GEO implementation checklist 1. **Map where your brand appears** in ChatGPT, Gemini, Perplexity, and Google AI. 2. **Find out which questions matter** by category, product, and intent. 3. **Rewrite introductions** with declarative language and clear definitions. 4. **Adjust titles and headings** to increase overlap with real queries. 5. **Add data, dates, and sources** directly in the body. 6. **Review the chunkability** of each important section. 7. **Maintain semantic [HTML](/en/resources/glossary/html-the-web-s-markup-language) and relevant [structured data](/en/resources/glossary/structured-data)** on key pages, in line with visible content and the service's documentation. 8. **Allow AI crawlers** in robots.txt. 9. **Increase brand mentions** in lists, reviews, PR, and trusted databases. 10. **Monitor generative [share of voice](/en/resources/glossary/ai-share-of-voice)** and repeat the cycle. ### What Brazilian brands need to do differently Brazilian brands cannot copy an American strategy and translate it. Search behavior varies with language, cultural references, slang, regulatory context, geography, and trust in local sources. A shoe store in Campinas, for example, may rank well on Google for “women’s running shoes.” But if [AI searches](/en/resources/glossary/ai-search) for “best shoe for running 10 km with a sensitive knee and fast delivery in the interior of São Paulo state,” it will combine reviews, lists, local context, and natural language. The strategy needs to reflect this. The first step is measurement. The second is understanding how engines respond in PT-BR, with a Brazilian IP address, to real questions from Brazilians. And this is exactly where many brands are still in the dark. ## Get started now Want to analyze your brand's presence in AI responses? Explore [Tropk.ai](/en) and confirm the scope of questions, answers, sources, and auditing suited to your operation. ## Sources for further reading - [What Is GEO (Generative Engine Optimization)? Complete Guide 2026]() - [AI Features and Your Website | Google Search Central | Documentation | Google for Developers]() - [Latest Google Search Documentation Updates | Google Search Central | What's new | Google for Developers]() - [FAQPage - Schema.org Type]() - [SEO Starter Guide: The Basics | Google Search Central | Documentation | Google for Developers]() - [Creating Helpful, Reliable, People-First Content | Google Search Central | Documentation | Google for Developers]() - [Overview of OpenAI Crawlers]() - [GEO: Generative Engine Optimization]() - [The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations]() - [Generative Engine Optimization (GEO): Explanation & Algorithm Breakdown – First Page Sage]() - [“GEO: Generative Engine Optimization”]() - [35.2% of clicks]() - [93% of AI search interactions end without a click]() - [Wikipedia in Brazilian Portuguese]() - [1.2 million ChatGPT answers, 5,000 pages, and 7 verticals]() - [26% since 2023]() - [50% of consumers already use AI as their primary source of information]() - [62.5% market share among consumer AI tools in 2026]() - [3rd in the global ranking for weekly ChatGPT use]() - [54% of Brazil's population used generative AI in 2025]() - [27% in organic traffic to Amazon Brazil between January and July 2025]() - [16% for B2B traffic from ChatGPT]() - [11% of responses analyzed]() ## Frequently asked questions ### What is GEO, or Generative Engine Optimization? GEO, or Generative Engine Optimization, is the practice of improving the conditions for content to be found, understood, and cited in generative AI responses. The term was formalized in an academic study presented at KDD 2024. The gains measured in the experiment varied by technique and domain and do not guarantee results on any current site or engine. ### What is the difference between GEO and SEO? SEO improves the discoverability and organic presence of pages in search engines. GEO aims to increase the presence of sources and brands in AI-generated answers. The two practices are connected through technical accessibility, usefulness, and verifiable content. A well-ranked page may not be cited, and a citation does not automatically mean a click or recommendation. ### How can a website increase its chances of appearing in ChatGPT answers? A website can improve its chances of appearing in ChatGPT search by allowing access to OAI-SearchBot, keeping information public and verifiable, and organizing clear answers to relevant questions. Review titles, terms, sources, conditions, and content updates. GPTBot has a separate purpose related to training and independent controls. No configuration guarantees that ChatGPT will select or cite the page. ### What are query fan-out and fan-out queries in AI search? Query fan-out is the breakdown of a question into related searches to investigate subtopics and sources. Fan-out queries are these complementary searches. Google documents the possible use of this technique in AI Overviews and AI Mode, and studies of ChatGPT have observed query expansion. For content, this suggests covering relevant questions beyond a single keyword. ### What is RAG, Retrieval-Augmented Generation? RAG, Retrieval-Augmented Generation, combines external information retrieval with text generation by a language model. An implementation may split documents into passages and retrieve relevant parts for a question before composing the answer. Retrieval methods vary, and not every source consulted will be cited. RAG does not eliminate errors or guarantee that a brand will appear. ### Is schema markup required for GEO? Schema markup is not a universal GEO requirement and does not guarantee citation by AI. Structured data helps describe entities and content and should match the visible text. For AI Overviews and AI Mode, Google does not require special schema. FAQPage remains in Schema.org, although FAQ rich results were discontinued on Google in May 2026. ## About the author [Luiz Gardelin]() 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. --- [All pages in Markdown]()