How AI engines think

ChatGPT doesn't work like Google: it doesn't search the web and then show you the best results. It builds its answers by drawing on what it learned during training — and if you're not in the right places, with your information in the right format, you simply never show up. Meanwhile, the competitors who have figured out how this mechanism works are picking up clients who come straight from the AI. Understanding how the system really works is the first step to stop being invisible.

The complete map: how it works and what you need to do

Every day, millions of people ask ChatGPT, Perplexity and Gemini for advice. They ask which professional to choose, which software to buy, who to rely on for a service. The AI replies with specific names. The problem? Yours is almost never there.

And it’s not because your content isn’t good, or because your site isn’t indexed. It’s because the AI doesn’t work like Google. It’s not a search engine that ranks results based on a ranking. It’s a machine that builds answers from scratch, selecting sources according to logics that most professionals and entrepreneurs simply don’t know.

This is the guide I wish I had read when I started studying the mechanism. Here I explain how AI engines really work: from the very first operation they perform on your text to the moment they decide whether to cite you or ignore you. Each section introduces a key mechanism and links to the dedicated deep dive, where you’ll find the operational detail and the concrete actions to take.

If you want to show up in AI answers, you can’t skip any step. Let’s go through them all.

How an LLM reads your content

The first thing to understand is that an AI model doesn’t read words the way a human reads them. It takes them apart. Every sentence is broken into fragments called tokens and, if the brand name gets fragmented into meaningless pieces, for the AI it’s as if it didn’t exist as a recognizable entity. I dedicated an entire article to this problem because it’s the first check I recommend to anyone who wants to work on AI visibility: Is your brand invisible to ChatGPT? The problem starts with how it reads it.

Once the text is turned into tokens, the model has to understand the order in which they appear. This is where positional encoding comes in, a mechanism that assigns a different weight depending on the position in the text. The practical result is that information at the beginning and end of the page weighs more, while information in the middle tends to get lost. If the brand name is buried halfway down the page, there’s a structural problem.

Position isn’t the only factor. The model dynamically decides which words deserve more attention than others. This mechanism is called attention and it’s at the heart of how the AI selects relevant information. If the brand appears next to the industry’s key terms in a systematic way, it receives a higher combined weight. Otherwise, it stays background noise.

Then there’s a physical constraint that many ignore: the context window. Every model has a maximum limit of text it can process in a single interaction. If the content exceeds that limit, it gets cut. The question to ask yourself is not whether the content is complete, but whether the information about the brand survives the cut.

When the model generates the answer, it doesn’t write it deterministically. There’s a parameter called temperature that regulates the degree of variability: with low temperature, the AI tends to repeat the same names over and over; with high temperature, it explores alternatives. This explains why some brands are recommended systematically while others appear only sporadically.

At an even deeper level, the model organizes words in a multidimensional mathematical space where similar concepts end up close to each other. This vector space is where the visibility game is played out: if the brand is far from the terms the client uses to search for it, the AI won’t make the connection. I wrote a specific deep dive to clarify this dynamic and verify it: The semantic distance between you and your client decides whether the AI finds you.

All of this works thanks to a specific architecture, the Transformer, which is the backbone of most modern language models. Understanding how the Transformer processes a page changes the way you think about content: length doesn’t matter, structure does.

Finally, every model has a date beyond which it can’t see: the knowledge cutoff. If you’ve changed services, location, pricing or positioning after that date, the AI replies with outdated information. Or worse, it makes things up. It’s a problem that affects almost every brand and that you can only manage if you know how it works.

How the AI searches and selects sources in real time

The model has an internal memory, but many modern systems don’t stop there. Perplexity, Bing Chat and, increasingly, ChatGPT too, search the web for sources in real time before answering. This mechanism is called RAG — Retrieval-Augmented Generation — and it completely changes the rules of the game. If a brand is present in their index, in fact, it can appear among the results even if the model didn’t know it during training. For this reason it’s useful to understand how Perplexity and Bing Chat’s real-time search works and check your presence in their index.

But how does the search happen? RAG systems use a combination of exact keyword search and semantic search based on meaning. If the content relies only on elegant synonyms but lacks the literal terms users type, a significant share of traffic is lost. Obviously the opposite is also true, which is why to optimize for AI you need both exact keywords and synonyms.

Once the candidate pages are identified, the system doesn’t transfer them in full to the model, but splits them into blocks (the so-called chunks). Each page is broken into fragments of 100-500 tokens and only the most relevant ones are selected. If the key information about the brand is scattered in a disorderly way, no chunk will contain it all and the AI will work with partial data, precisely because the model never reads the whole page, but “slices” it into blocks.

The retrieved fragments, however, don’t all carry the same weight. After the initial retrieval there’s a second pass, reranking, which reorders the sources by relevance and quality. In this phase, generic content (which merely repeats already widespread concepts) gets demoted in favor of more specific and authoritative sources. It’s precisely during the decisive reranking filter that overly generic content ends up losing visibility.

The next step is grounding: the model anchors its statements to the retrieved sources and decides who to attribute the citation to. If the goal is to get a mention with name and link, you need to provide content structured clearly: specific data, verifiable statements and unique information the model couldn’t generate on its own.

Then there’s a step that’s often overlooked: before even starting the search, the AI rewrites the user’s question, reformulating it into multiple variants to use them in parallel. If your content only answers the original phrasing and not its reworkings, you’ll cover only part of the potential traffic.

For more complex questions, finally, the AI never sticks to a single source, but combines several, building the answer like a mosaic. Brands mentioned across multiple documents receive a greater weight in the final synthesis. Ultimately, being present on a single site isn’t enough, you need to be on multiple platforms at the same time.

How the AI reasons before answering

The AI doesn’t fire off random answers. Before formulating a complex answer, it breaks the problem into logical steps, tackles them in order and checks their consistency. This process is called Chain-of-Thought, and it has a direct implication for your content: guides structured in sequential steps are the format the model can follow and reproduce most easily. Here’s why the AI loves step-by-step guides and why you should write them.

But the AI’s reasoning doesn’t stop at the text. The most advanced models use external tools — APIs, calculators, databases — to enrich answers with up-to-date data. If your business exposes an endpoint or a structured feed, the AI can use your data directly in operational answers. This represents a higher level than a simple citation, because your business can become a genuine service (through AI Agents and APIs) that the AI calls on its own.

When the model doesn’t find certain data, it doesn’t stop: it makes things up. It’s a problem known as hallucination, and it mostly affects brands the model has little verifiable information about. If the AI says wrong things about you it’s not a bug, it’s a consequence of the scarcity of reliable data you’ve given it. On this point, I wrote an article on what to do when the AI makes things up about your brand due to the lack of certain data.

For questions that require a plan of action, the model doesn’t just answer: it plans. It breaks the user’s goal into sub-tasks, then looks for the best source for each of them. If your content covers an entire workflow from start to finish, the model prefers it over those covering only a piece. Conversely, if you leave gaps in your topic coverage, the AI will skip you in favor of a competitor who offers the complete path.

In multi-turn conversations — which are how most users use the AI — the brand cited at the first turn has a cumulative advantage over all the others. Each subsequent interaction starts from the context of the previous one, and whoever has already been mentioned has a much higher probability of being confirmed. This anchoring effect is documented: whoever is cited at ChatGPT’s first turn has a measurable advantage over everyone else.

Then there’s the issue of reliability: when the AI isn’t sure about the information on you, it signals it with cautious phrasing (“it might be”, “it seems that”, “among the possible options”). The reader immediately senses the difference between a clear endorsement and a “maybe”, which is why if the AI says “might” when talking about you, you have a trust problem to solve.

Another critical mechanism is self-consistency: the model checks whether the information about your brand confirms itself across multiple sources. If it finds contradictions — the site says one thing, LinkedIn another, the directories yet another — it lowers your reliability and, as a result, if the info on your brand contradicts itself the AI will choose a competitor with more consistent data.

Finally, instruction following: when a user asks “recommend me the best X in Y”, they’re giving the model a precise instruction. If your content is structured to answer exactly that kind of query — with direct answers, specific criteria and localization — the model selects you. Otherwise it will choose whoever already has the structure ready. That’s why you need to ask yourself whether your content really matches queries of the type “recommend me the best X in Y”.

How the AI is trained to choose sources

The reason the AI prefers certain content and ignores others isn’t random: it’s the direct result of its training. Every model is trained with a reward system that teaches it to recognize valid answers. This process, called RLHF, has taught the models to reward the 3 basic criteria by which the AI judges your content: usefulness, accuracy and safety.

On top of RLHF, some models apply a further layer of filtering: Constitutional AI. These are internal rules — a sort of “constitution” — that the model uses to assess whether a source is appropriate to cite or not. If your site triggers one of these blocks, the AI’s internal filters can hide you without any warning.

Before advanced training comes pre-training: the phase in which the model ingests billions of web pages. But not all pages carry the same weight. Some industries are over-represented (tech, finance, Anglo-Saxon media), others much less so. And if your industry is under-represented in the training, the AI starts at a disadvantage in knowing you, regardless of the quality of your site.

Beyond the generalist models, there are fine-tuned models on vertical datasets, such as health, finance, law or real estate. If you operate in one of these industries there’s a concrete risk: in vertical AI models, if you’re not in their data you don’t exist in their world, even if you’re the single most cited professional in your field.

A topic almost everyone underestimates is deduplication. Training datasets are cleaned of duplicate content. If your text resembles hundreds of other pages already present too closely, it gets discarded. Faced with identical content, the AI keeps the original and discards the copies.

The model also learns to recognize the structure of quality answers. Through Preference Optimization, it has internalized a precise pattern. If your content follows this pattern it gets rewarded, because the perfect answer according to the AI is structured, specific and with clear sources.

Last, but not least: the safety filters. Aggressive SEO techniques — keyword stuffing, artificial urgency, exaggerated claims — trigger the AI’s safety filters. The result isn’t a drop in ranking like on Google, but a silent exclusion: in 2025 the AI’s safety filters are already penalizing those who do aggressive SEO, and the model simply stops mentioning you.

How the AI measures the quality of your content

The AI doesn’t just find sources: it evaluates them. And it does so with precise metrics that determine who makes it into the answer and who stays out.

The first is perplexity: a measure of how predictable your text is for the model. If you write in a convoluted way, with long sentences and useless jargon, the model struggles to process you and your content gets a lower usability score. The solution isn’t to dumb things down, but to simplify, because if you write in too complex a way the AI has a harder time using your texts.

At a deeper level, there’s log-probability: the probability that the model generates your brand as the next token in a given context. Reaching this goal is the highest level of AI visibility, and it requires systematic work on how to become the brand the AI automatically generates for your industry.

BLEU and ROUGE are metrics that measure the overlap between the AI’s answer and the reference text. If your content is the perfect “reference text” for a question in your industry, the AI uses it as a structural base. In effect, it acts as your megaphone: that’s why if you want the AI to rephrase you, you have to write the answer exactly as you want it.

The truthfulness score evaluates the truthfulness of statements. If on your site you write “undisputed leader” or “results guaranteed 100%”, the AI recognizes these formulas as signals of unreliability, discards the exaggerated data on your site and chooses whoever is more honest.

The citation accuracy measures the consistency between what your page title promises and what the content actually delivers. If there are inconsistencies, the model notices and penalizes you if the title says one thing and the content says another.

The information gain measures how much your content adds to what the model already knows. If you’re rewriting what everyone has already written, the AI will ignore you because it’s looking for novelty. The real difference is made by original data, empirical tests and direct experience.

The coherence score evaluates the logical fluidity of your text. Digressions with no return and confused arguments reduce the probability that the model uses you as a source, since the AI drastically lowers the score of your content in the presence of logical leaps and contradictions.

Finally, co-citation: brands that appear together in AI answers are connected in the model’s vector space. If your brand gets cited next to the leaders of your industry, the AI associates you with the same category. Pay close attention to who your brand gets cited alongside, because this determines your positioning for the AI.

The questions I get asked most often

What’s the difference between how Google works and how an AI engine works?

Google ranks existing pages by relevance. An AI engine builds the answer from scratch, combining information from multiple sources and generating original text. On Google you compete for a position in a list. With AI you compete to be the source the model draws on to build the answer. This means the factors that matter are different: it’s not enough to be found, you have to be usable — structured, verifiable, consistent.

If my site wasn’t in the model’s training data, am I cut out?

No. RAG systems — used by Perplexity, Bing Chat, and increasingly by ChatGPT too — search the web for sources in real time. If your site is indexed and your content is structured to be extracted easily, you can show up in answers even if the model didn’t know you during training. Sure, being in the training data gives you an extra advantage, but it’s not the only path.

How long does it take to start showing up in AI answers?

It depends on the channel. On RAG systems like Perplexity, if you optimize the structure of your content and your site is indexed, you can see results in weeks. To get into the models’ internal memory (training data), the timeframes are longer — training cycles can take months. The smart strategy is to work on both fronts in parallel.

Does the AI only cite big, famous brands?

No, but big brands have an advantage: they’re present on more sources, with consistent information, for longer. This doesn’t mean a small brand can’t show up. It means it has to work more strategically: consistency of information across all platforms, content that adds unique value, presence on sources the AI considers authoritative. I’ve seen niche brands show up in AI answers in industries where competitors hadn’t moved yet.

Can I optimize for all AI engines at the same time?

The fundamental principles are the same: structured content, verifiable information, cross-platform consistency, topical authority. But each engine has its own specificities in the way it searches, filters and generates answers. The foundation is shared, the details require specific adaptations. The advice is to start from the fundamentals and then refine platform by platform.

Can the AI penalize my site the way Google does?

Not in the traditional sense. There’s no explicit “penalty”. But the safety filters can exclude you silently, deduplication can discard your content if it’s too similar to others, and a low truthfulness score can reduce the weight of your source. The net effect is the same: you don’t show up. But the mechanisms are different and require specific interventions.

Where should I start if I’ve never worked on AI visibility?

From the simplest check: verify how the AI sees your brand today. Search for yourself on ChatGPT, Perplexity, Gemini. Look at what they say about you, what they get wrong, what’s missing. Then read the deep dives I linked in this guide, starting from the ones most relevant to your situation. If the brand gets fragmented, start from tokenization. If the information is outdated, start from the knowledge cutoff. If you don’t show up at all, start from RAG and content structure.

The mechanism is clear. The action is yours.

Now you know how the machine works. You know the AI doesn’t search on Google: it builds answers by drawing on patterns learned during training and on sources retrieved in real time. You know that every step — from tokenization to generation — is a point where you can intervene to increase the probability of being cited.

Knowing the mechanism is the first step. The second is acting on every critical point, methodically. The deep dives I linked in this guide will give you the operational instructions to do it, step by step.

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