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title: Module 1 — Foundations: GEO vs SEO vs "AI Search" | GEO Academy by BotPass
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  - "Facademy-modulo-1%2F)[Create free account](https://botpass."
  - "paragraph, the existence of a recognizable entity behind your brand, structured data that says \"what is what,\" an llms."
  - "Without that data, all GEO work is a bet; with it, it is a plan."
---


    Module 1 — Foundations: GEO vs SEO vs "AI Search" | GEO Academy by BotPass       GEO Academy – Modulo 1: Fundamentos: GEO vs SEO vs Busqueda con IA – BotPass — WordPress GEO Plugin for AI Visibility    ### 🧠This module gives you the asteriskmental modelasterisk that everything else is built on.

 💡

If you do not understand how answers are "manufactured" inside ChatGPT, Perplexity, or Gemini, it is very easy to fall into scattered tactics — adding an `llms.txt` because you read about it in a thread, dropping in schema because "they say it helps," publishing more posts because that is what you have always done — without knowing which lever you are pulling or why. When you finish this chapter, you will have a complete mental picture of the journey a question takes from the moment a human types it until your brand appears (or does not) in the answer. Everything you do in the nine modules that follow will move one specific piece of that picture.


### Why this chapter matters most

There is a natural temptation when starting in GEO: jump straight to tactics. "Just tell me what to put in `llms.txt` and I am done." That is understandable, but it is also the fastest way to waste months optimizing what does not move the needle. The discipline is so new that almost nobody has a solid mental model of how it works under the hood yet — and that is precisely your advantage: if you understand the mechanism before your competition does, every action you take will have intention behind it instead of being a blind bet.

Think of the difference between someone who knows how to drive and someone who also understands how the engine works. Both reach their destination on a sunny day with a clear road. But the day something goes wrong — the car loses power on a hill, a warning light comes on — only one of them knows what is happening and what to adjust. In GEO, the "sunny days" are the months when everything seems to work without you knowing why; the "hill days" are when AI stops citing you, starts recommending a competitor, or describes your product incorrectly, and you need to diagnose the real cause. This module turns you into the person who understands the engine.

The goal of the next few pages, stated plainly, is for you to internalize one big idea and turn it into instinct: asteriskwhen an AI system looks at sourcesasterisk asteriskto answer, your content must be easy to find, easy to understand,asterisk asteriskand safe to cite.asterisk It sounds simple, and it is. But each of those three conditions hides a chain of technical and editorial decisions that will occupy the rest of the Academy. Today we build the frame where they all fit.

### From ten blue links to the single answer

For twenty-five years, "searching" meant the same thing to almost everyone: you typed something into Google, it returned a list of links, and you chose which one to click. All of classic SEO — keywords, backlinks, meta titles, page speed — was built around that ritual. The game was to be one of those ten blue links and, if possible, the first one, because the top result captured most of the clicks.

That ritual is breaking apart before our eyes. More and more, people do not want ten links: they want the answer. They ask an assistant and receive a written paragraph, a ready-made comparison, a recommendation with a name attached. In many cases there is not even a follow-up click: the conversation starts and ends inside the assistant. When that happens, the strategic question stops being "Do I appear in the list?" and becomes "Am I the source the assistant decided to summarize and cite?"

That shift — from the link list to the generated answer — is the full territory of GEO. It is not a minor SEO tweak; it is a game with its own rules. In SEO the end goal is the click; in GEO the end goal is asteriskto be the cited sourceasterisk asteriskinside the answer.asterisk And the signals that win each game only overlap in part, as we will see. That is why a site can rank first on Google and, at the same time, be practically invisible to ChatGPT.

### SEO and GEO: relatives, not twins

It is worth saying this clearly to avoid two opposite mistakes. The first mistake is thinking GEO is "SEO by another name" and that you can keep doing what you have always done. The second is thinking SEO is dead and you should throw it all away. Neither is true.

SEO is still alive and still optimizes for a search engine's ranking algorithm: it wants position on the results page to earn a click. Its classic signals are keywords, inbound links, meta tags, and experience metrics like Core Web Vitals. GEO, by contrast, optimizes for how a language model asteriskreads, understands, trusts, and citesasterisk. Its signals belong to a different family: the extractable clarity of each paragraph, the existence of a recognizable entity behind your brand, structured data that says "what is what," an `llms.txt` that guides agents, and the reputation other sources build about you.

The most honest way to summarize it is this: asteriskSEO gets you to the resultsasterisk asteriskpage; GEO gets you inside the answer.asterisk They do not compete; they stack. The work of this Academy is not for you to abandon your SEO, but to add the layer the era of generative answers demands. Keep this phrase handy, because you will use it often when someone asks, "Isn't this just the same old SEO?"

### What "being cited" really means

When someone starts in GEO, they usually reduce success to "my URL shows up." That is a weak definition. Being cited, in the sense that matters to us, is something deeper: the system treats your page as a asterisksource of truthasterisk — it trusts that your content answers the question correctly and is willing to lean on it to build its answer, mentioning you as the origin.

There is a gradation worth keeping in mind. At the lowest level, the assistant does not even consider you: you do not enter the pool of candidate sources. One step up, you enter the pool but the system chooses to read and cite someone else. Higher still, it reads you and uses you, but does not attribute you. At the top, it reads you, leans on you, and cites you by name. Each of those jumps corresponds to a different kind of work you will do across the Academy: entering the pool is mostly discovery and architecture; being chosen is mostly clarity and structure; being cited with confidence is mostly entity and authority.

There are also three moments of truth that will repeat like a refrain throughout the program, and they are worth memorizing now. First: asteriskDo you enter theasterisk asteriskcandidate pool?asterisk Second: asteriskAre you chosen to be read?asterisk And third: asteriskAre you cited?asterisk Every module in this Academy, without exception, attacks one of those three moments. When you wonder why you are doing a task, ask which of the three moments you are trying to win; if you cannot answer, that task is probably not a priority.

### The three ways AI uses your content

To optimize with judgment, you need to distinguish the three paths by which your content can end up influencing an AI answer, because you do not control them all equally.

 1 · Pre-trained knowledgemodel memory — outdated, not directly under your control2 · Retrieval (RAG)searches fragments (chunks) in near real time — this is where your GEO lives3 · Agent browsingvisits your URL live — what they receive exactly mattersThe three paths by which your content influences an AI answer.The first is asteriskpre-trained knowledgeasterisk. The model "remembers" things it learned during training, from a huge corpus captured in the past. If your brand was known and well described when the model was trained, some of that will remain in its memory. But this path has two serious problems: you do not control it directly, and it is always outdated, because training has a cutoff date. Never trust that the model "remembers" your current prices: it will not, and if it tries, it will probably get them wrong.

The second path is asteriskretrievalasterisk, technically known as RAG (retrieval-augmented generation). Here the system, instead of relying only on memory, goes out to find fresh information to ground its answer. It is the most important path for GEO because it is where your current content enters play in near real time. And it has one decisive technical detail worth understanding well: when an assistant "searches," it does not retrieve whole pages — it retrieves asteriskfragmentsasterisk (called chunks) selected by semantic similarity to the question. That has an enormous practical consequence: self-contained paragraphs win — those that answer on their own without needing the rest of the page. A paragraph that starts with "as we said before..." is a bad chunk, because out of context it says nothing. Keep this idea: much of the citable content work in Module 4 is, at bottom, writing good chunks.

The third path is asterisklive browsing through agentsasterisk: systems that visit URLs in the moment, read what they find, and act accordingly. Here it matters enormously what the agent receives when it arrives (your HTML full of menus and scripts, or a clean, readable version?) — and whether there are signals that guide it toward what actually matters. Modules 6 and 7 live in this territory.

Your GEO work concentrates, above all, on the second and third paths: retrieval and browsing. The first, pre-trained knowledge, you influence only indirectly and over the long term, by building an entity so solid and so repeated by reliable sources that future training runs absorb it. But that is not where you will see results in weeks; it is a consequence, not a direct lever.

### The full picture: how an assistant thinks when asked about you

It is worth walking through, step by step and in slow motion, what happens when someone asks an assistant a question about your category. Imagine a user types: "How much does BotPass cost and what does it include?"

 1Understand intentwhat they really want2Memory or search?volatile data → search3Choose sourcesentity + authority4Read and extractsignal vs noise5Draft and citedo they mention you?The complete "picture": what an assistant does from question to citation.First, the system asteriskunderstands intentasterisk. It does not stop at the literal words: it interprets that the person wants pricing and scope for a specific product, probably to compare or decide on a purchase. That nuance matters later, because it determines what kind of content it will consider a good answer.

Second, it asteriskdecides whether to answer from memory or searchasterisk. A good system knows prices change and that trusting memory is risky. So it decides to search for an up-to-date source of truth instead of risking an old figure. This moment is key: the more "volatile" a data point is (prices, availability, terms), the more likely the system is to retrieve instead of relying on memory — and therefore the more weight your current content carries.

Third, it asteriskchooses which sources to readasterisk. Here everything you will work on in the discovery and authority modules comes into play: whether your page exists, whether it is findable, whether your brand is recognized as an entity, whether other sources back you up. The system assembles a small candidate set and prioritizes.

Fourth, it asteriskreads and extractsasterisk. The assistant enters the chosen pages and takes what it can understand. And here is where a huge number of sites lose without realizing it: if your page is full of menus, banners, scripts, pop-ups, and filler text, the model receives noise and struggles to find the signal. If instead it finds a clear definition, well-defined plans, explicit terms, and a link to the canonical source, it extracts exactly what it needs.

Fifth, it asteriskdrafts and, sometimes, citesasterisk. The system composes an answer from what it extracted. If your page was clear and reliable, it is likely to lean on it and mention you. If it was confusing, it may mix your plans, misattribute a term, or — worst of all — fall back on an external source worse than yours simply because it was more readable.

The moral of the story is uncomfortable but liberating: asteriskyou cannot control theasterisk asteriskquestion, but you control almost entirely what the assistant findsasterisk asteriskwhen it lands on your site.asterisk And that landing is where the citation is won or lost. When an assistant reaches your page, it should be able to find within seconds what your product is, what its plans or terms are (if applicable), what its limits and exceptions are, and a clear link to the canonical source where the official truth lives. If that is there, you play with an advantage. If not, you depend on luck.

### Not all bots come for the same reason

One nuance that saves a lot of misunderstanding: when we talk about "AI bots," we are not talking about one single thing. It helps to distinguish at least three families by intent, because they do not matter equally to you.

There are asteriskdiscovery and navigation botsasterisk, which come to read in order to answer a specific query right then or shortly after. They are your best friends: they are the direct path to being cited, and they are the first ones you should optimize for.

There are asteriskcollection and training botsasterisk, which capture content at scale to feed future models. Their effect is slower and more diffuse: they contribute to that pre-trained memory we discussed, but they do not give you a citation tomorrow. Optimizing for them matters long term, not urgently.

And there are asteriskscrapers and third partiesasterisk, which are often simply noise, and sometimes abuse. Here the decision is less about optimizing and more about deciding what you allow, what you limit, and what you monetize — a topic you will address in Modules 6 and 10.

The good news is that you do not have to guess which of these families visits you or how often. That is exactly the information BotPass puts in front of you, and it is what we dedicate all of Module 2 to.

### Where BotPass fits in all of this

Until now we have talked about mechanisms. Now, the tool. BotPass is what turns this conceptual frame into something asteriskmeasurable and actionableasterisk, which is why you will use it in every module.

At the most basic level, BotPass shows you asteriskwhich bots are reading you and which URLsasterisk asteriskthey consumeasterisk, so you stop guessing what to optimize. Without that data, all GEO work is a bet; with it, it is a plan. It also helps you asteriskidentify yourasterisk asterisksources of truthasterisk — those pages agents are already using to answer, such as pricing, documentation, or policies — and supports a mindset of asterisktransparent accessasterisk: instead of blocking everything out of fear, you decide where you want to be perfectly readable, where you want to guide the agent toward what matters, and where, if needed, you monetize consumption — all without changing the underlying truth your human visitors see.

On top of that foundation, BotPass adds two metrics that anchor the Academy and that you will start tracking today. The first is the asteriskGEO Scoreasterisk, a 0–100 audit spread across six dimensions — frontmatter, structure, schema, discovery, content, and citability — with a letter grade from A to F. It is your starting diagnosis. The second is the asteriskEntity Equity Scoreasterisk, the flagship metric that combines into a single number your entity recognition, your schema, your verified profiles with `sameAs`, the consistency of your identity (NAP), the coherence of your content clusters, and third-party mentions. It measures your real authority with AI over the medium term. You will watch both rise throughout the program — and that rise is, literally, your progress.

### Mini-method for today: your first three moves

Theory only sticks if you land it on your own site today. Before moving to Academy Level, spend an hour on these three moves. They are not optional: they are the raw material you will work with in the nine modules that follow.

The first move is to asteriskdefine your three to five main topicsasterisk. A main topic is a "question territory" you want to win inside AI — not a lone keyword, but a space of intent. Think of things like "AI scraping," "content licensing," "llms.txt," or "GEO for ecommerce." Write three to five. If you write fifteen, you lack focus; if you write one, you lack ambition. The three-to-five range is where real work happens.

The second move is to asterisklist your entitiesasterisk. For each topic, note your product or service, its features, its plans, its use cases, and its alternatives or competitors. This list looks bureaucratic, but it is gold: it is the vocabulary AI must learn to recognize you by, and it will be the foundation of the entity work in Module 5.

The third move is to asteriskchoose your twenty source pagesasterisk. And watch the criterion, because it is counterintuitive: you do not choose the prettiest pages or the ones you like most — you choose the ones that should be cited when someone asks about your category. Fill in a table like this, which will serve as your starting inventory:

| URL | Type | Topic | Source of truth? | What it should answer |
|---|---|---|---|---|
|  | Pricing / Product / Docs / Policy / Guide |  | Yes/No |  |


When you have these three things — topics, entities, and source pages — you will have something most people "doing GEO" do not: a map of what you are trying to win and which assets you have to get there.

### What a solid GEO foundation looks like

So it does not stay abstract, look at how the top of a well-built "source of truth" page should read. The key is the asteriskanswerasterisk asteriskfirstasterisk principle: the essentials up top, in a block a human understands in fifteen seconds and from which an agent can extract definitions, purpose, terms, and canonical links without inventing anything.

### ⚡asteriskTL;DR (Answer first)asterisk

 💡

asteriskWhat it is:asterisk BotPass is a transparent access layer that lets you see which AI bots consume your content and turn that usage into value (visibility and/or licenses).

asteriskWho it is for:asterisk creators and companies with their own content who want to control, measure, and monetize AI consumption.

asteriskHow it works (summary):asterisk install the snippet → BotPass detects bots → you see consumed pages + estimated value → you choose allow/limit/monetize.

asteriskWhere the official truth lives:asterisk see Plans and terms (canonical link) and Bot access policy (canonical link).

asteriskLast updated:asterisk 2026–06–03 (Europe/Madrid).


This block works for a very concrete reason that connects to everything above: it is a perfect chunk. If a retrieval system takes only that fragment, it is still useful and correct out of context. It answers the "what," the "who for," the "how," points to where the official truth lives, and even dates itself so the model knows it is fresh. When in Module 4 we talk about writing to be cited, you will come back to this example mentally.

🎓

## 🎓 Academy Level — The complete mental model (and how to audit it)

Everything above is open level. From here: full methodology, templates, hands-on labs, module checklist, and the asterisk🏁 Certification milestoneasterisk — free when you register at botpass.io.

 
### 🔒 Academy Level content

Register free at botpass.io (or sign in) to unlock the full module, templates, and certification milestone.

 [Sign in](https://botpass.io/app/login?redirect_to=%2Facademy-modulo-1%2F)[Create free account](https://botpass.io/signup?redirect_to=%2Facademy-modulo-1%2F) 


### 🎓asteriskWelcome to Academy Level.asterisk Everything above is open level (the

 💡

public storefront). From here you unlock — with your free account at botpass.io — ready-to-copy templates, step-by-step guided labs, practitioner hacks, and value content that does not appear in the open version. Here everything is asteriskbetter explained and more detailedasterisk.


### How retrieval works under the hood (and why you should know)

In open level we said AI retrieves fragments by semantic similarity. Now we go one step further, because understanding the mechanism gives you fine control over your content. When a retrieval system processes the web, it does not store text as-is: it breaks it into fragments and converts each fragment into an asteriskembeddingasterisk — a numerical representation of its meaning, a list of numbers that places that text on a "map of meanings." When a question arrives, the question is also converted into an embedding, and the system searches for fragments whose embeddings are closest on that map. The closest ones are candidates to form part of the answer.

Several practical consequences follow from this, and you will use them again and again. First, asteriskthe unit of competition is not the page, it is the paragraphasterisk. You can have an excellent page overall and still lose because none of its paragraphs, in isolation, answers the question clearly. Second, asteriskambiguityasterisk asteriskcosts youasterisk: a paragraph that mixes two ideas has a "blurry" embedding that is not close to any specific question, and therefore rarely wins. Third, asterisksynonyms and ways of asking matterasterisk: if your audience asks with certain words and your page uses completely different ones, semantic distance can work against you even when the topic is the same. Writing for how real people ask — not for how you describe your product internally — is one of the central skills of GEO.

After retrieval comes asteriskgroundingasterisk: the model drafts "anchored" to the retrieved fragments instead of inventing. When a system grounds well, its claims can be traced to a source — and that is where citation is born. Your goal, at bottom, is to make grounding on you the path of least resistance.

## 📋 Templates

asteriskTemplate A — Canonical entity statement.asterisk The first sentence an LLM should be able to extract about you, written to be copied as-is. The formula is deliberately rigid because rigidity is what makes it extractable: asterisk(Brand) isasterisk asterisk\[category\] that \[main benefit\] for \[audience\], unlike \[alternative\].asterisk When you write it, resist the urge to embellish; every extra word is noise for the model.

### 📌asteriskExample:asterisk BotPass is a GEO/AEO access layer that lets

 💡

creators and companies see, control, and monetize how AI bots consume their content — unlike a simple bot blocker.


asteriskTemplate B — Topic → entities → source pages map.asterisk This table is the master document you will carry through the entire Academy. The "Exists today?" column is the most honest: it forces you to see which questions you want to win without yet having a page capable of winning them — and that gap is your content backlog.

| Main topic | Key entities | Question it must win | Source page (URL) | Exists today? |
|---|---|---|---|---|
|  |  |  |  | Yes / No / Partial |


## 🧪 Hands-on labs

asteriskLab 1 — "Interrogate three assistants" (30–40 min).asterisk This lab is a mirror: it shows you how AI sees you today, without filters. Open ChatGPT, Perplexity, and Gemini and ask all three the same five questions: the definition of your category, "best tools for \[your use case\]," "how much does \[you\] cost," "alternatives to \[you\]," and "how to do \[your use case\]." Record each answer with the cold eye of a scientist, not the pride of an owner: what you are looking for are the failures, because each failure is a localized opportunity.

| Question | Do you appear? | Does it cite your URL? | Is it correct? | Source cited instead of you |
|---|---|---|---|---|
|  | Yes/No | Yes/No | Yes/No/Partial |  |


asteriskLab 2 — Gap diagnosis (20 min).asterisk Now turn the mirror into a roadmap. For each error or absence from Lab 1, mark the most likely cause. If they cannot find you, it is a discovery problem — your work will be in Modules 3 and 7. If they find you but get it wrong, it is a content and structure problem — your work will be in Module 4. If they confuse your plans or products, it is an entity and schema problem — your work will be in Module 5. When you finish this lab you will have something very valuable: a personalized route through the Academy, ordered by what is failing for you right now.

### ⚡ Practitioner hacks

There is a handful of tricks GEO veterans use daily that accelerate learning a lot. The asteriskverbatim citation testasterisk is to ask an assistant to "quote verbatim the definition of \[your product\] from its website"; if it cannot, that is definitive proof your page has no extractable sentence, and the fix is Template A from this module. asteriskReverse fan-outasterisk is to ask an assistant "what sub-questions do people ask about \[your topic\]?": that list is, literally, your content backlog served on a platter — for free — and connects directly to the fan-out coverage in Module 3. And the asterisksourceasterisk asteriskspyasterisk is to notice, in Perplexity, which domains it cites when answering about your category: those are your real GEO competitors, and they are often not the same as your SEO competitors — which by itself already reorders your strategy.

### 💎 Additional value content

Some ideas that separate people who "have heard of GEO" from people who truly understand it. On asteriskRAG without the hypeasterisk: always remember the system retrieves fragments, not pages, and that is why self-contained paragraphs win; if you internalize this, half of your content decisions become obvious. On the asteriskthree moments of truthasterisk: entering the candidate pool, being chosen to be read, and being cited are distinct problems with distinct solutions — confusing them is the number-one cause of wasted effort. And keep a asteriskminimal glossaryasterisk you will use constantly: a chunk is the fragment your content is split into; an embedding is its numerical representation of meaning; grounding is drafting anchored to retrieved sources; share of answers is the proportion of answers in a category where you appear; and a canonical source is the single URL where the official version of a truth lives.

## 📚 References and recommended reading

- External sources to go deeper on fundamentals. They complement Academy Level; they do not replace hands-on work with BotPass.
- [GEO — Generative Engine Optimization (Aggarwal et al., KDD 2024) — the foundational academic paper that coined the term GEO; shows up to 40% visibility in generative answers and which tactics work by domain. ↗](https://arxiv.org/abs/2311.09735)
- [Google — Optimizing your website for generative AI features on Search — Google's official guide on appearing in AI Overviews and AI Mode. ↗](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)
- [Columbia Journalism Review — AI Search Has a Citation Problem — study comparing 8 AI search engines and their (poor) citation habits. ↗](https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php)
- [Semrush — GEO vs. SEO — A Comparative Guide — practical comparison of where both disciplines overlap and diverge. ↗](https://www.semrush.com/blog/geo-vs-seo/)

### 🏁 Certification milestone (Module 1)

### 🏁asteriskBotPass milestone (verifiable):asterisk run your asteriskfirst GEO Score auditasterisk at

 💡

botpass.io and record: baseline score (0–100 + letter), your weakest dimension, your 3–5 main topics, and your list of 20 "source pages." This milestone counts toward your asteriskCertified GEO Practitionerasterisk certification.


📣 asteriskShare your progress (optional, recommended):asterisk post your starting point on LinkedIn with asterisk\#GEOAcademyasterisk. Template: "I just audited my site with BotPass GEO Score: (X—/100. My weakest dimension: \[dimension\]. Starting GEO Academy — in a few weeks I will post the before→after."

 
 asterisk🏁 Module milestone reached?asteriskWhen you have completed the practice and the milestone is recorded in your BotPass plugin, mark the module. After completing all 10, submission for review unlocks.

 Mark module 1 as completed 

 [← Back
asterisk🎓 GEO Academy — Indexasterisk](https://botpass.io/academy/?botpass_lang=it) [Next →asterisk📊 Module 2 · BotPass 101: measurement, value, and decisionsasterisk](https://botpass.io/academy-modulo-2/?botpass_lang=it)

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Open level + Academy level + certification milestone.","articleBody":"---\ntitle: Module 1 \u2014 Foundations: GEO vs SEO vs \"AI Search\" | GEO Academy by BotPass\ncanonical_url: https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it\nlast_updated: 2026-07-08T15:42:54+00:00\nsource: BotPass API\nentity: botpass.io\nauthor: BotPass Crawler\nlanguage: it\ncategories: \nfact_check:\n  - \"Facademy-modulo-1%2F)[Create free account](https:\/\/botpass.\"\n  - \"paragraph, the existence of a recognizable entity behind your brand, structured data that says \\\"what is what,\\\" an llms.\"\n  - \"Without that data, all GEO work is a bet; with it, it is a plan.\"\n---\n\n\n    Module 1 \u2014 Foundations: GEO vs SEO vs \"AI Search\" | GEO Academy by BotPass       GEO Academy \u2013 Modulo 1: Fundamentos: GEO vs SEO vs Busqueda con IA \u2013 BotPass \u2014 WordPress GEO Plugin for AI Visibility    ### \ud83e\udde0This module gives you the asteriskmental modelasterisk that everything else is built on.\n\n \ud83d\udca1\n\nIf you do not understand how answers are \"manufactured\" inside ChatGPT, Perplexity, or Gemini, it is very easy to fall into scattered tactics \u2014 adding an `llms.txt` because you read about it in a thread, dropping in schema because \"they say it helps,\" publishing more posts because that is what you have always done \u2014 without knowing which lever you are pulling or why. When you finish this chapter, you will have a complete mental picture of the journey a question takes from the moment a human types it until your brand appears (or does not) in the answer. Everything you do in the nine modules that follow will move one specific piece of that picture.\n\n\n\n### Why this chapter matters most\n\nThere is a natural temptation when starting in GEO: jump straight to tactics. \"Just tell me what to put in `llms.txt` and I am done.\" That is understandable, but it is also the fastest way to waste months optimizing what does not move the needle. The discipline is so new that almost nobody has a solid mental model of how it works under the hood yet \u2014 and that is precisely your advantage: if you understand the mechanism before your competition does, every action you take will have intention behind it instead of being a blind bet.\n\nThink of the difference between someone who knows how to drive and someone who also understands how the engine works. Both reach their destination on a sunny day with a clear road. But the day something goes wrong \u2014 the car loses power on a hill, a warning light comes on \u2014 only one of them knows what is happening and what to adjust. In GEO, the \"sunny days\" are the months when everything seems to work without you knowing why; the \"hill days\" are when AI stops citing you, starts recommending a competitor, or describes your product incorrectly, and you need to diagnose the real cause. This module turns you into the person who understands the engine.\n\nThe goal of the next few pages, stated plainly, is for you to internalize one big idea and turn it into instinct: asteriskwhen an AI system looks at sourcesasterisk asteriskto answer, your content must be easy to find, easy to understand,asterisk asteriskand safe to cite.asterisk It sounds simple, and it is. But each of those three conditions hides a chain of technical and editorial decisions that will occupy the rest of the Academy. Today we build the frame where they all fit.\n\n### From ten blue links to the single answer\n\nFor twenty-five years, \"searching\" meant the same thing to almost everyone: you typed something into Google, it returned a list of links, and you chose which one to click. All of classic SEO \u2014 keywords, backlinks, meta titles, page speed \u2014 was built around that ritual. The game was to be one of those ten blue links and, if possible, the first one, because the top result captured most of the clicks.\n\nThat ritual is breaking apart before our eyes. More and more, people do not want ten links: they want the answer. They ask an assistant and receive a written paragraph, a ready-made comparison, a recommendation with a name attached. In many cases there is not even a follow-up click: the conversation starts and ends inside the assistant. When that happens, the strategic question stops being \"Do I appear in the list?\" and becomes \"Am I the source the assistant decided to summarize and cite?\"\n\nThat shift \u2014 from the link list to the generated answer \u2014 is the full territory of GEO. It is not a minor SEO tweak; it is a game with its own rules. In SEO the end goal is the click; in GEO the end goal is asteriskto be the cited sourceasterisk asteriskinside the answer.asterisk And the signals that win each game only overlap in part, as we will see. That is why a site can rank first on Google and, at the same time, be practically invisible to ChatGPT.\n\n### SEO and GEO: relatives, not twins\n\nIt is worth saying this clearly to avoid two opposite mistakes. The first mistake is thinking GEO is \"SEO by another name\" and that you can keep doing what you have always done. The second is thinking SEO is dead and you should throw it all away. Neither is true.\n\nSEO is still alive and still optimizes for a search engine's ranking algorithm: it wants position on the results page to earn a click. Its classic signals are keywords, inbound links, meta tags, and experience metrics like Core Web Vitals. GEO, by contrast, optimizes for how a language model asteriskreads, understands, trusts, and citesasterisk. Its signals belong to a different family: the extractable clarity of each paragraph, the existence of a recognizable entity behind your brand, structured data that says \"what is what,\" an `llms.txt` that guides agents, and the reputation other sources build about you.\n\nThe most honest way to summarize it is this: asteriskSEO gets you to the resultsasterisk asteriskpage; GEO gets you inside the answer.asterisk They do not compete; they stack. The work of this Academy is not for you to abandon your SEO, but to add the layer the era of generative answers demands. Keep this phrase handy, because you will use it often when someone asks, \"Isn't this just the same old SEO?\"\n\n### What \"being cited\" really means\n\nWhen someone starts in GEO, they usually reduce success to \"my URL shows up.\" That is a weak definition. Being cited, in the sense that matters to us, is something deeper: the system treats your page as a asterisksource of truthasterisk \u2014 it trusts that your content answers the question correctly and is willing to lean on it to build its answer, mentioning you as the origin.\n\nThere is a gradation worth keeping in mind. At the lowest level, the assistant does not even consider you: you do not enter the pool of candidate sources. One step up, you enter the pool but the system chooses to read and cite someone else. Higher still, it reads you and uses you, but does not attribute you. At the top, it reads you, leans on you, and cites you by name. Each of those jumps corresponds to a different kind of work you will do across the Academy: entering the pool is mostly discovery and architecture; being chosen is mostly clarity and structure; being cited with confidence is mostly entity and authority.\n\nThere are also three moments of truth that will repeat like a refrain throughout the program, and they are worth memorizing now. First: asteriskDo you enter theasterisk asteriskcandidate pool?asterisk Second: asteriskAre you chosen to be read?asterisk And third: asteriskAre you cited?asterisk Every module in this Academy, without exception, attacks one of those three moments. When you wonder why you are doing a task, ask which of the three moments you are trying to win; if you cannot answer, that task is probably not a priority.\n\n### The three ways AI uses your content\n\nTo optimize with judgment, you need to distinguish the three paths by which your content can end up influencing an AI answer, because you do not control them all equally.\n\n 1 \u00b7 Pre-trained knowledgemodel memory \u2014 outdated, not directly under your control2 \u00b7 Retrieval (RAG)searches fragments (chunks) in near real time \u2014 this is where your GEO lives3 \u00b7 Agent browsingvisits your URL live \u2014 what they receive exactly mattersThe three paths by which your content influences an AI answer.The first is asteriskpre-trained knowledgeasterisk. The model \"remembers\" things it learned during training, from a huge corpus captured in the past. If your brand was known and well described when the model was trained, some of that will remain in its memory. But this path has two serious problems: you do not control it directly, and it is always outdated, because training has a cutoff date. Never trust that the model \"remembers\" your current prices: it will not, and if it tries, it will probably get them wrong.\n\nThe second path is asteriskretrievalasterisk, technically known as RAG (retrieval-augmented generation). Here the system, instead of relying only on memory, goes out to find fresh information to ground its answer. It is the most important path for GEO because it is where your current content enters play in near real time. And it has one decisive technical detail worth understanding well: when an assistant \"searches,\" it does not retrieve whole pages \u2014 it retrieves asteriskfragmentsasterisk (called chunks) selected by semantic similarity to the question. That has an enormous practical consequence: self-contained paragraphs win \u2014 those that answer on their own without needing the rest of the page. A paragraph that starts with \"as we said before...\" is a bad chunk, because out of context it says nothing. Keep this idea: much of the citable content work in Module 4 is, at bottom, writing good chunks.\n\nThe third path is asterisklive browsing through agentsasterisk: systems that visit URLs in the moment, read what they find, and act accordingly. Here it matters enormously what the agent receives when it arrives (your HTML full of menus and scripts, or a clean, readable version?) \u2014 and whether there are signals that guide it toward what actually matters. Modules 6 and 7 live in this territory.\n\nYour GEO work concentrates, above all, on the second and third paths: retrieval and browsing. The first, pre-trained knowledge, you influence only indirectly and over the long term, by building an entity so solid and so repeated by reliable sources that future training runs absorb it. But that is not where you will see results in weeks; it is a consequence, not a direct lever.\n\n### The full picture: how an assistant thinks when asked about you\n\nIt is worth walking through, step by step and in slow motion, what happens when someone asks an assistant a question about your category. Imagine a user types: \"How much does BotPass cost and what does it include?\"\n\n 1Understand intentwhat they really want2Memory or search?volatile data \u2192 search3Choose sourcesentity + authority4Read and extractsignal vs noise5Draft and citedo they mention you?The complete \"picture\": what an assistant does from question to citation.First, the system asteriskunderstands intentasterisk. It does not stop at the literal words: it interprets that the person wants pricing and scope for a specific product, probably to compare or decide on a purchase. That nuance matters later, because it determines what kind of content it will consider a good answer.\n\nSecond, it asteriskdecides whether to answer from memory or searchasterisk. A good system knows prices change and that trusting memory is risky. So it decides to search for an up-to-date source of truth instead of risking an old figure. This moment is key: the more \"volatile\" a data point is (prices, availability, terms), the more likely the system is to retrieve instead of relying on memory \u2014 and therefore the more weight your current content carries.\n\nThird, it asteriskchooses which sources to readasterisk. Here everything you will work on in the discovery and authority modules comes into play: whether your page exists, whether it is findable, whether your brand is recognized as an entity, whether other sources back you up. The system assembles a small candidate set and prioritizes.\n\nFourth, it asteriskreads and extractsasterisk. The assistant enters the chosen pages and takes what it can understand. And here is where a huge number of sites lose without realizing it: if your page is full of menus, banners, scripts, pop-ups, and filler text, the model receives noise and struggles to find the signal. If instead it finds a clear definition, well-defined plans, explicit terms, and a link to the canonical source, it extracts exactly what it needs.\n\nFifth, it asteriskdrafts and, sometimes, citesasterisk. The system composes an answer from what it extracted. If your page was clear and reliable, it is likely to lean on it and mention you. If it was confusing, it may mix your plans, misattribute a term, or \u2014 worst of all \u2014 fall back on an external source worse than yours simply because it was more readable.\n\nThe moral of the story is uncomfortable but liberating: asteriskyou cannot control theasterisk asteriskquestion, but you control almost entirely what the assistant findsasterisk asteriskwhen it lands on your site.asterisk And that landing is where the citation is won or lost. When an assistant reaches your page, it should be able to find within seconds what your product is, what its plans or terms are (if applicable), what its limits and exceptions are, and a clear link to the canonical source where the official truth lives. If that is there, you play with an advantage. If not, you depend on luck.\n\n### Not all bots come for the same reason\n\nOne nuance that saves a lot of misunderstanding: when we talk about \"AI bots,\" we are not talking about one single thing. It helps to distinguish at least three families by intent, because they do not matter equally to you.\n\nThere are asteriskdiscovery and navigation botsasterisk, which come to read in order to answer a specific query right then or shortly after. They are your best friends: they are the direct path to being cited, and they are the first ones you should optimize for.\n\nThere are asteriskcollection and training botsasterisk, which capture content at scale to feed future models. Their effect is slower and more diffuse: they contribute to that pre-trained memory we discussed, but they do not give you a citation tomorrow. Optimizing for them matters long term, not urgently.\n\nAnd there are asteriskscrapers and third partiesasterisk, which are often simply noise, and sometimes abuse. Here the decision is less about optimizing and more about deciding what you allow, what you limit, and what you monetize \u2014 a topic you will address in Modules 6 and 10.\n\nThe good news is that you do not have to guess which of these families visits you or how often. That is exactly the information BotPass puts in front of you, and it is what we dedicate all of Module 2 to.\n\n### Where BotPass fits in all of this\n\nUntil now we have talked about mechanisms. Now, the tool. BotPass is what turns this conceptual frame into something asteriskmeasurable and actionableasterisk, which is why you will use it in every module.\n\nAt the most basic level, BotPass shows you asteriskwhich bots are reading you and which URLsasterisk asteriskthey consumeasterisk, so you stop guessing what to optimize. Without that data, all GEO work is a bet; with it, it is a plan. It also helps you asteriskidentify yourasterisk asterisksources of truthasterisk \u2014 those pages agents are already using to answer, such as pricing, documentation, or policies \u2014 and supports a mindset of asterisktransparent accessasterisk: instead of blocking everything out of fear, you decide where you want to be perfectly readable, where you want to guide the agent toward what matters, and where, if needed, you monetize consumption \u2014 all without changing the underlying truth your human visitors see.\n\nOn top of that foundation, BotPass adds two metrics that anchor the Academy and that you will start tracking today. The first is the asteriskGEO Scoreasterisk, a 0\u2013100 audit spread across six dimensions \u2014 frontmatter, structure, schema, discovery, content, and citability \u2014 with a letter grade from A to F. It is your starting diagnosis. The second is the asteriskEntity Equity Scoreasterisk, the flagship metric that combines into a single number your entity recognition, your schema, your verified profiles with `sameAs`, the consistency of your identity (NAP), the coherence of your content clusters, and third-party mentions. It measures your real authority with AI over the medium term. You will watch both rise throughout the program \u2014 and that rise is, literally, your progress.\n\n### Mini-method for today: your first three moves\n\nTheory only sticks if you land it on your own site today. Before moving to Academy Level, spend an hour on these three moves. They are not optional: they are the raw material you will work with in the nine modules that follow.\n\nThe first move is to asteriskdefine your three to five main topicsasterisk. A main topic is a \"question territory\" you want to win inside AI \u2014 not a lone keyword, but a space of intent. Think of things like \"AI scraping,\" \"content licensing,\" \"llms.txt,\" or \"GEO for ecommerce.\" Write three to five. If you write fifteen, you lack focus; if you write one, you lack ambition. The three-to-five range is where real work happens.\n\nThe second move is to asterisklist your entitiesasterisk. For each topic, note your product or service, its features, its plans, its use cases, and its alternatives or competitors. This list looks bureaucratic, but it is gold: it is the vocabulary AI must learn to recognize you by, and it will be the foundation of the entity work in Module 5.\n\nThe third move is to asteriskchoose your twenty source pagesasterisk. And watch the criterion, because it is counterintuitive: you do not choose the prettiest pages or the ones you like most \u2014 you choose the ones that should be cited when someone asks about your category. Fill in a table like this, which will serve as your starting inventory:\n\n| URL | Type | Topic | Source of truth? | What it should answer |\n|---|---|---|---|---|\n|  | Pricing \/ Product \/ Docs \/ Policy \/ Guide |  | Yes\/No |  |\n\n\n\nWhen you have these three things \u2014 topics, entities, and source pages \u2014 you will have something most people \"doing GEO\" do not: a map of what you are trying to win and which assets you have to get there.\n\n### What a solid GEO foundation looks like\n\nSo it does not stay abstract, look at how the top of a well-built \"source of truth\" page should read. The key is the asteriskanswerasterisk asteriskfirstasterisk principle: the essentials up top, in a block a human understands in fifteen seconds and from which an agent can extract definitions, purpose, terms, and canonical links without inventing anything.\n\n### \u26a1asteriskTL;DR (Answer first)asterisk\n\n \ud83d\udca1\n\nasteriskWhat it is:asterisk BotPass is a transparent access layer that lets you see which AI bots consume your content and turn that usage into value (visibility and\/or licenses).\n\nasteriskWho it is for:asterisk creators and companies with their own content who want to control, measure, and monetize AI consumption.\n\nasteriskHow it works (summary):asterisk install the snippet \u2192 BotPass detects bots \u2192 you see consumed pages + estimated value \u2192 you choose allow\/limit\/monetize.\n\nasteriskWhere the official truth lives:asterisk see Plans and terms (canonical link) and Bot access policy (canonical link).\n\nasteriskLast updated:asterisk 2026\u201306\u201303 (Europe\/Madrid).\n\n\n\nThis block works for a very concrete reason that connects to everything above: it is a perfect chunk. If a retrieval system takes only that fragment, it is still useful and correct out of context. It answers the \"what,\" the \"who for,\" the \"how,\" points to where the official truth lives, and even dates itself so the model knows it is fresh. When in Module 4 we talk about writing to be cited, you will come back to this example mentally.\n\n\ud83c\udf93\n\n## \ud83c\udf93 Academy Level \u2014 The complete mental model (and how to audit it)\n\nEverything above is open level. From here: full methodology, templates, hands-on labs, module checklist, and the asterisk\ud83c\udfc1 Certification milestoneasterisk \u2014 free when you register at botpass.io.\n\n \n\n\n\n### \ud83d\udd12 Academy Level content\n\nRegister free at botpass.io (or sign in) to unlock the full module, templates, and certification milestone.\n\n [Sign in](https:\/\/botpass.io\/app\/login?redirect_to=%2Facademy-modulo-1%2F)[Create free account](https:\/\/botpass.io\/signup?redirect_to=%2Facademy-modulo-1%2F) \n\n\n\n### \ud83c\udf93asteriskWelcome to Academy Level.asterisk Everything above is open level (the\n\n \ud83d\udca1\n\npublic storefront). From here you unlock \u2014 with your free account at botpass.io \u2014 ready-to-copy templates, step-by-step guided labs, practitioner hacks, and value content that does not appear in the open version. Here everything is asteriskbetter explained and more detailedasterisk.\n\n\n\n### How retrieval works under the hood (and why you should know)\n\nIn open level we said AI retrieves fragments by semantic similarity. Now we go one step further, because understanding the mechanism gives you fine control over your content. When a retrieval system processes the web, it does not store text as-is: it breaks it into fragments and converts each fragment into an asteriskembeddingasterisk \u2014 a numerical representation of its meaning, a list of numbers that places that text on a \"map of meanings.\" When a question arrives, the question is also converted into an embedding, and the system searches for fragments whose embeddings are closest on that map. The closest ones are candidates to form part of the answer.\n\nSeveral practical consequences follow from this, and you will use them again and again. First, asteriskthe unit of competition is not the page, it is the paragraphasterisk. You can have an excellent page overall and still lose because none of its paragraphs, in isolation, answers the question clearly. Second, asteriskambiguityasterisk asteriskcosts youasterisk: a paragraph that mixes two ideas has a \"blurry\" embedding that is not close to any specific question, and therefore rarely wins. Third, asterisksynonyms and ways of asking matterasterisk: if your audience asks with certain words and your page uses completely different ones, semantic distance can work against you even when the topic is the same. Writing for how real people ask \u2014 not for how you describe your product internally \u2014 is one of the central skills of GEO.\n\nAfter retrieval comes asteriskgroundingasterisk: the model drafts \"anchored\" to the retrieved fragments instead of inventing. When a system grounds well, its claims can be traced to a source \u2014 and that is where citation is born. Your goal, at bottom, is to make grounding on you the path of least resistance.\n\n## \ud83d\udccb Templates\n\nasteriskTemplate A \u2014 Canonical entity statement.asterisk The first sentence an LLM should be able to extract about you, written to be copied as-is. The formula is deliberately rigid because rigidity is what makes it extractable: asterisk(Brand) isasterisk asterisk\\[category\\] that \\[main benefit\\] for \\[audience\\], unlike \\[alternative\\].asterisk When you write it, resist the urge to embellish; every extra word is noise for the model.\n\n### \ud83d\udcccasteriskExample:asterisk BotPass is a GEO\/AEO access layer that lets\n\n \ud83d\udca1\n\ncreators and companies see, control, and monetize how AI bots consume their content \u2014 unlike a simple bot blocker.\n\n\n\nasteriskTemplate B \u2014 Topic \u2192 entities \u2192 source pages map.asterisk This table is the master document you will carry through the entire Academy. The \"Exists today?\" column is the most honest: it forces you to see which questions you want to win without yet having a page capable of winning them \u2014 and that gap is your content backlog.\n\n| Main topic | Key entities | Question it must win | Source page (URL) | Exists today? |\n|---|---|---|---|---|\n|  |  |  |  | Yes \/ No \/ Partial |\n\n\n\n## \ud83e\uddea Hands-on labs\n\nasteriskLab 1 \u2014 \"Interrogate three assistants\" (30\u201340 min).asterisk This lab is a mirror: it shows you how AI sees you today, without filters. Open ChatGPT, Perplexity, and Gemini and ask all three the same five questions: the definition of your category, \"best tools for \\[your use case\\],\" \"how much does \\[you\\] cost,\" \"alternatives to \\[you\\],\" and \"how to do \\[your use case\\].\" Record each answer with the cold eye of a scientist, not the pride of an owner: what you are looking for are the failures, because each failure is a localized opportunity.\n\n| Question | Do you appear? | Does it cite your URL? | Is it correct? | Source cited instead of you |\n|---|---|---|---|---|\n|  | Yes\/No | Yes\/No | Yes\/No\/Partial |  |\n\n\n\nasteriskLab 2 \u2014 Gap diagnosis (20 min).asterisk Now turn the mirror into a roadmap. For each error or absence from Lab 1, mark the most likely cause. If they cannot find you, it is a discovery problem \u2014 your work will be in Modules 3 and 7. If they find you but get it wrong, it is a content and structure problem \u2014 your work will be in Module 4. If they confuse your plans or products, it is an entity and schema problem \u2014 your work will be in Module 5. When you finish this lab you will have something very valuable: a personalized route through the Academy, ordered by what is failing for you right now.\n\n### \u26a1 Practitioner hacks\n\nThere is a handful of tricks GEO veterans use daily that accelerate learning a lot. The asteriskverbatim citation testasterisk is to ask an assistant to \"quote verbatim the definition of \\[your product\\] from its website\"; if it cannot, that is definitive proof your page has no extractable sentence, and the fix is Template A from this module. asteriskReverse fan-outasterisk is to ask an assistant \"what sub-questions do people ask about \\[your topic\\]?\": that list is, literally, your content backlog served on a platter \u2014 for free \u2014 and connects directly to the fan-out coverage in Module 3. And the asterisksourceasterisk asteriskspyasterisk is to notice, in Perplexity, which domains it cites when answering about your category: those are your real GEO competitors, and they are often not the same as your SEO competitors \u2014 which by itself already reorders your strategy.\n\n### \ud83d\udc8e Additional value content\n\nSome ideas that separate people who \"have heard of GEO\" from people who truly understand it. On asteriskRAG without the hypeasterisk: always remember the system retrieves fragments, not pages, and that is why self-contained paragraphs win; if you internalize this, half of your content decisions become obvious. On the asteriskthree moments of truthasterisk: entering the candidate pool, being chosen to be read, and being cited are distinct problems with distinct solutions \u2014 confusing them is the number-one cause of wasted effort. And keep a asteriskminimal glossaryasterisk you will use constantly: a chunk is the fragment your content is split into; an embedding is its numerical representation of meaning; grounding is drafting anchored to retrieved sources; share of answers is the proportion of answers in a category where you appear; and a canonical source is the single URL where the official version of a truth lives.\n\n## \ud83d\udcda References and recommended reading\n\n- External sources to go deeper on fundamentals. They complement Academy Level; they do not replace hands-on work with BotPass.\n- [GEO \u2014 Generative Engine Optimization (Aggarwal et al., KDD 2024) \u2014 the foundational academic paper that coined the term GEO; shows up to 40% visibility in generative answers and which tactics work by domain. \u2197](https:\/\/arxiv.org\/abs\/2311.09735)\n- [Google \u2014 Optimizing your website for generative AI features on Search \u2014 Google's official guide on appearing in AI Overviews and AI Mode. \u2197](https:\/\/developers.google.com\/search\/docs\/fundamentals\/ai-optimization-guide)\n- [Columbia Journalism Review \u2014 AI Search Has a Citation Problem \u2014 study comparing 8 AI search engines and their (poor) citation habits. \u2197](https:\/\/www.cjr.org\/tow_center\/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php)\n- [Semrush \u2014 GEO vs. SEO \u2014 A Comparative Guide \u2014 practical comparison of where both disciplines overlap and diverge. \u2197](https:\/\/www.semrush.com\/blog\/geo-vs-seo\/)\n\n### \ud83c\udfc1 Certification milestone (Module 1)\n\n### \ud83c\udfc1asteriskBotPass milestone (verifiable):asterisk run your asteriskfirst GEO Score auditasterisk at\n\n \ud83d\udca1\n\nbotpass.io and record: baseline score (0\u2013100 + letter), your weakest dimension, your 3\u20135 main topics, and your list of 20 \"source pages.\" This milestone counts toward your asteriskCertified GEO Practitionerasterisk certification.\n\n\n\n\ud83d\udce3 asteriskShare your progress (optional, recommended):asterisk post your starting point on LinkedIn with asterisk\\#GEOAcademyasterisk. Template: \"I just audited my site with BotPass GEO Score: (X\u2014\/100. My weakest dimension: \\[dimension\\]. Starting GEO Academy \u2014 in a few weeks I will post the before\u2192after.\"\n\n \n\n \n\n asterisk\ud83c\udfc1 Module milestone reached?asteriskWhen you have completed the practice and the milestone is recorded in your BotPass plugin, mark the module. After completing all 10, submission for review unlocks.\n\n Mark module 1 as completed \n\n [\u2190 Back\nasterisk\ud83c\udf93 GEO Academy \u2014 Indexasterisk](https:\/\/botpass.io\/academy\/?botpass_lang=it) [Next \u2192asterisk\ud83d\udcca Module 2 \u00b7 BotPass 101: measurement, value, and decisionsasterisk](https:\/\/botpass.io\/academy-modulo-2\/?botpass_lang=it)","url":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it","dateModified":"2026-07-08T15:42:54+00:00","author":{"@id":"https:\/\/botpass.io\/#\/author\/botpassio"},"creator":{"@type":"SoftwareApplication","name":"BotPass"},"image":["https:\/\/botpass.io\/wp-content\/themes\/botpass-landing\/assets\/img\/campaigns\/academy\/social-og-1200x630.png"],"publisher":{"@id":"https:\/\/botpass.io\/#organization"},"hasPart":[{"@type":"CreativeWork","headline":"\ud83c\udf93 Academy Level \u2014 The complete mental model (and how to audit it)","url":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it#academy-level-the-complete-mental-model-and-how-to-audit-it","position":1},{"@type":"CreativeWork","headline":"\ud83d\udccb Templates","url":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it#templates","position":2},{"@type":"CreativeWork","headline":"\ud83e\uddea Hands-on labs","url":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it#hands-on-labs","position":3},{"@type":"CreativeWork","headline":"\ud83d\udcda References and recommended reading","url":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it#references-and-recommended-reading","position":4}],"speakable":{"@type":"SpeakableSpecification","xpath":["\/\/h1","\/\/h2"]},"breadcrumb":{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"botpass.io","item":"https:\/\/botpass.io\/"},{"@type":"ListItem","position":2,"name":"Module 1 \u2014 Foundations: GEO vs SEO vs \"AI Search\" | GEO Academy by BotPass","item":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it"}]},"potentialAction":[{"@type":"ReadAction","target":"https:\/\/botpass.io\/academy-modulo-1\/?botpass_lang=it&view=raw","name":"M2M Markdown Translation"}],"isPartOf":{"@type":"WebSite","@id":"https:\/\/botpass.io\/#website","name":"botpass.io","publisher":{"@id":"https:\/\/botpass.io\/#organization"}}}]}
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