What is training data?
Training data is the collection of text, code and other content that an AI model learns from before it is released. For a large language model, it typically includes public web pages, books, code, licensed content and data prepared by people. For the largest models, it runs to trillions of tokens.
During training, the model does not copy pages into a library. It learns patterns: how language works, which facts are stated often and which brands are linked to which topics, products and opinions. The exact recipe is rarely published in full, but AI companies generally describe a mix of publicly available, licensed and human-created data.
Much of the public part is gathered by crawlers. OpenAI's GPTBot collects pages that may be used to train its models, and CCBot builds Common Crawl, a free web archive that many AI developers use. Collection stops at the model's knowledge cutoff.
Why training data matters for your brand
Training data forms a model's default picture of your company. When an assistant answers without searching the web, everything it says about you comes from patterns in that data: whether you are mentioned at all, what you are known for and how you compare with competitors. A brand that is described often, consistently and positively across many sources leaves a stronger impression than one that only talks about itself.
AI companies release new models regularly, trained on newer data. Today's web presence becomes tomorrow's training data, so coverage you earn now can shape answers for a long time. Read more about how AI models decide which brands to recommend.
How to influence training data
Decide your crawler policy. Check which AI crawlers your robots.txt allows. Blocking training crawlers such as GPTBot keeps your pages out of future training sets, but models may then know less about you. OpenAI handles GPTBot separately from its search crawler, OAI-SearchBot.
Publish clear, factual content. Explain what you do, for whom and how you differ, in plain sentences that are easy to learn from.
Earn mentions on other sites. Reviews, trade media, directories and community discussions widen your footprint beyond your own domain.
Stay consistent. Use the same name, description and key facts everywhere, so the model learns one clear picture.
Trackbase Crawler Analytics shows which AI crawlers visit your site and what they read, so you can see whether training crawlers are collecting your content.
Examples of training data in practice
A B2B payroll software vendor that has appeared in HR trade media for years is described in detail by a model answering from memory, while a newer rival is barely known.
A Dutch bicycle webshop that blocked all AI crawlers finds that assistants answering from memory know little about its range.
A recipe blog allows OAI-SearchBot but blocks GPTBot, so its pages can still appear in ChatGPT search while staying out of future OpenAI model training.
A software company's old prices, repeated on many comparison sites, keep appearing in answers because they dominated the data the model was trained on.
Frequently asked questions
Can I find out if my website is in an AI model's training data?
Usually not directly, because AI companies rarely publish full lists of training sources. You can check your server logs or crawler analytics for training crawlers such as GPTBot and CCBot, which shows whether your pages were collected. Asking a model about your brand without web search also gives a rough idea of how well it knows you.
Should I block AI crawlers from using my content for training?
It depends on your goals. Blocking training crawlers keeps your content out of future models, which matters most for publishers whose content is the product. For most brands, being well represented in training data helps models know and recommend them. You can also block training while allowing search crawlers, since several AI companies use separate crawlers for each purpose.
Does removing information from my website remove it from AI models?
Not from models that are already trained. What a model learned stays in it until a newer version replaces it. Removing or correcting a page does help assistants that search the web, and it affects what future models can learn. For outdated facts, publish a clear correction and update third-party sources as well.