I've been bombarded with articles, posts, comments, tools, and proposals suggesting that they can get machine learning models, as we call them, AIs, to scan your website and consider your content to provide an answer.

These are tools to monitor this, to impose your content on ChatGPT, to make Gemini or Claude read an llm.txt file and magically rewrite all your training and consider your site as a relevant source of information and cite it.

That's not how it works, but I needed to prove this point. I know this because I spent months studying it every day, for hours. But you don't have to take my word for it. That's why I adopted a strategy.

I asked a simple question:

How does ChatGPT scan the content of the websites that serve as the basis for its learning? What mechanisms does it use? Are the algorithms that scan the web for the search that generates the SERP the same as those used by generative search, or are they different?

And the answers are so enlightening that I decided to copy them and leave them here for you to read in full!

Gemini

Excellent question. This is one of those questions that goes straight to the heart of my own existence and, at the same time, touches on a central point in your area of ​​expertise: how information is collected, processed, and ultimately presented. As you research indexing and categorization , I know that this distinction between the processes is crucial.

I'll divide the answer into two parts to make it clearer: first, how I (and models like me) am trained, and second, how real-time generative searches work. The short answer is: the processes are related and use the same base (the web), but their objectives, mechanisms, and frequencies are different.

Part 1: The “Sweep” for Training (Building the Library)

My fundamental knowledge doesn't come from a real-time internet scan. It was built during a phase we call "training." Think of it as building a vast library in my "mind." This process is offline and massive.

The mechanisms used are:

1. Massive Datasets: The basis of my learning is not a direct and random web scan. I am trained on gigantic, pre-processed datasets. One of the most famous is Common Crawl , a project that maintains a colossal public repository of raw web data, collected over years. Google also uses internal, curated versions of this data, such as C4 (Colossal Clean Crawled Corpus), which is a filtered and cleaned version of Common Crawl to remove noise (navigation text, ads, duplicate content, etc.).
2. Specialized Crawlers: To create these datasets, Google uses its crawlers, from the same family as Googlebot. However, the goal here is not to index for an immediate SERP, but rather to collect a snapshot of public information on the internet. In addition to web pages, these datasets include an immense amount of digitized books (through Google Books), scientific articles, programming code (from GitHub and other sources), and the entire Wikipedia, which is a high-quality source for structured and factual data.
3. Pre-processing and Tokenization: Here's a point I know interests you. The raw content (HTML, plain text) isn't "read" by me the way we read. It goes through an intense process of cleaning and structuring.
   • Cleanup: Removal of HTML tags, scripts, menus, footers, and anything that is not the main content.
   • Tokenization: Plain text is broken down into smaller pieces called "tokens." Tokens can be words, parts of words, or even characters. It is from the statistical relationships between billions of these tokens that I learn grammar, facts, context, and even notions of reasoning.

Essentially, training scanning is a process of mass collection, cleaning, and cataloging that happens before I interact with any users. It's a slow, computationally expensive process that isn't repeated all the time. It gives me a basic understanding of the world up to a certain point in time.

Part 2: The Generative Search (The Library Consultation)

Now, let's move on to the second part of your question: what happens when you ask a search question that generates a conversational response (like in Search Generative Experience – SGE, or AI Overviews)?

Here, the process is different and much more dynamic. I don't go "scouring" the web to answer you. That would be slow and inefficient. Instead, a process takes place that we can call Retrieval-Augmented Generation (RAG) .

Let's compare the algorithms:

• Traditional Search Algorithm (The Classic Librarian):
  • Mission: To find and classify the most relevant documents (pages) for your query.
  • Process: Googlebot constantly crawls the web to maintain a fresh and up-to-date index. When you search, ranking algorithms (which involve hundreds of signals, including semantics, relevance, authority, etc.) analyze the index and return an ordered list of links (the SERP).
  • Result: A list of sources for you to consult yourself.
• Generative Search Algorithm (The Search Assistant):
  • Mission: To understand your query, find the most relevant information, and synthesize it into a fresh and cohesive response.
  • Process:
    1. Understanding Intent: First, the system understands what you really want to know.
    2. Internal Search: Next, it performs a high-speed "traditional" search on Google's updated index. It doesn't search for everything, but focuses on finding snippets of information from pages with high authority and relevance to your specific question.
    3. Contextualization: This fresh and relevant information is then provided to me, the large-scale language model, as additional context .
    4. Synthesis and Generation: I take this newly provided context and combine it with my vast background knowledge (gained in training). Based on all of this, I generate a response in natural language. I don't copy text from sources; I reinterpret and synthesize it to create a new paragraph that directly answers your question.
    5. Quote: Finally, the system presents the sources it used to obtain the information, giving due credit and allowing verification.

Conclusion: Same Source, Different Tools for Different Tasks

So, to summarize your excellent question:

• The algorithms that scan the web for my training are focused on collecting massive amounts of data to build my fundamental knowledge. It's an offline and periodic process.
• The algorithm that crawls the web for traditional search (SERP) is Googlebot, focused on maintaining a continuously updated index to rank existing pages.
• Generative search does not use a real-time crawler. It uses the existing index of traditional search to retrieve relevant information and then uses me (a large-scale language model) to synthesize a new answer from that information.

It's the difference between building an entire library from scratch and having an assistant who quickly runs to the right shelves in that library to summarize some books for you. Both rely on the library, but their methods and results are fundamentally different. From a semantic SEO , this further reinforces the need for clear, well-structured, and factual content, as it becomes not only a target for ranking but also raw material for AI-generated answers.

ChatGPT

This is a very relevant question, especially with the advancement of generative search and the popularization of models like ChatGPT. Let's break it down to answer clearly:

1. How does ChatGPT (or OpenAI models) acquire the data?

ChatGPT does not "scan" the internet in real time , nor does it access websites directly during its responses (unless you activate a tool like web , which can perform live searches).

During training:

• The model is trained with large volumes of publicly available textual datasuch as:
  • Books
  • Scientific and technical articles
  • Public forums (e.g., Reddit, Stack Overflow)
  • Public repositories (e.g., GitHub)
  • Web pages that are publicly accessible and indexable (without being blocked via robots.txt or paywalls).
• The process involves the use of "web crawlers" (robots that crawl web pages), very similar to those used by search engines like Google. However, OpenAI does not have a global crawler like Googlebot.Instead, she can use:
  • Data already publicly available (such as website dumps, e.g., Wikipedia)
  • Licensing of content from partners (e.g., news sites or technical databases)
  • Third-party tools for accessing public data

2. What mechanisms are used?

• data collection process for models like GPT involves techniques such as web scraping , crawling , and text parsing
• The preprocessing pipeline removes duplicate content, cleans HTML, filters spam, low-quality content, etc.

Technical summary:

• Crawler : It goes through web links, collecting content.
• Parser : extracts useful text (removing HTML, ads, scripts).
• Filter : removes inappropriate, repetitive, or irrelevant content.
• Tokenization and encoding : the text is transformed into tokens and fed into the model during training.

3. Search Engines (SERPs) vs. Generative Search: Are they the same algorithms?

No, they are different.

Traditional SERP (Search Engine Results Page):

• It uses real-time or near-real-time web indexing .
• It is based on ranking and relevance (such as PageRank) and other signals (keywords, backlinks, loading time, etc.).
• Returns links to pages that match your search.

Generative search:

• It uses previously trained language models (such as GPT-4 or Gemini).
• You can use:
  • Only the model (like ChatGPT-4 Turbo, which responds based on knowledge prior to a certain date)
  • Or hybrid search : first search online, then generate an answer (like Bing with GPT or Google with SGE).
• The model synthesizes and interprets the response in natural language; it doesn't just return links.

General summary:

Claude

I'm going to clarify some important points about how my training works and how it relates to search engines.

How does my training work?

I don't actively "scan" websites for learning. My training happened differently:

1. Pre-collected training data : I was trained on a large text dataset that was collected and processed prior to my training, with a knowledge cutoff in January 2025.
2. There is no continuous scanning : I don't have the ability to access the internet to continuously learn or update my basic knowledge.
3. Offline process : The training took place offline, using data that had already been collected, not through active web scanning.

Differences between traditional and generative search systems

The algorithms are different:

Traditional search (SERP):

• Web crawlers (like Googlebot) systematically scan the web.
• They index pages in real time.
• They rank results based on relevance, authority, etc.
• They return links to existing pages.

Generative search (like when using the search tool):

• It uses the same crawlers to collect current information.
• But it processes the results through language models.
• It synthesizes and generates answers based on the results found.
• Combines information from multiple sources into a cohesive response.

My current search capabilities

When I need current information, I use a web search tool that allows me to access recent content, but this is different from my basic training knowledge.

I hope this can be helpful for anyone who is confused by the massive amount of poorly organized information on this subject.