Training data is the large collection of text, images, code and other content used to teach an AI model how to understand and generate language. For large language models, much of it comes from the public web, alongside licensed datasets, books and human-written examples.
For marketers, training data shapes what an AI model "knows" about your brand before it ever searches the web. If your company was well represented and accurately described when a model was trained, it is more likely to come up correctly in answers that rely on memory alone.
What is training data?
A large language model (LLM) learns patterns by processing enormous amounts of example content. That content is its training data. The model doesn't store pages like a search index. It adjusts billions of internal settings, called parameters, so it can predict likely text.
The scale is large. The 2020 paper introducing GPT-3 described a 175-billion-parameter model whose training mix was weighted 60% toward filtered Common Crawl data, with the rest from curated web text, books and Wikipedia. Newer models generally don't publish that level of detail.
Today, AI companies describe their sources in broad terms. OpenAI, for example, says its models are developed using publicly available internet content, information accessed through third-party partnerships, and content provided by users, human trainers and researchers, with synthetic data playing a growing role.
Why training data matters for GEO
AI assistants answer questions in two broad ways. They draw on what the model learned in training, and they look things up live through search, a process known as grounding. Generative engine optimization has to account for both.
Training data matters most when an assistant answers without searching, or when it decides what to search for. A model that already associates your brand with a category is more likely to think of you. A model that has never seen your brand, or saw outdated information, may leave you out or get details wrong, which is one cause of AI hallucination.
Training data also has a cutoff date. Anything that happened after the model's data was collected isn't in its memory, so newer facts depend on live retrieval.
How training data is collected and used
Web crawling
A large share of training data comes from crawling public web pages. Some companies run their own AI crawlers, like OpenAI's GPTBot and Anthropic's ClaudeBot. Others use open datasets such as Common Crawl, a nonprofit archive that reports holding over 300 billion pages collected across 15 years.
Filtering and cleaning
Raw web data is noisy, so it's filtered for quality, duplicates and unwanted content. OpenAI says it does not intentionally collect content from sources known to be behind paywalls, and that it filters to reduce personal information.
Pretraining
The model learns general language and knowledge by predicting text across the full dataset. This is where most of what it "knows" about the world, including brands, comes from.
Fine-tuning and feedback
After pretraining, models are refined with smaller, curated datasets and human feedback to make them more helpful and safer. This stage shapes behavior more than factual knowledge.
Training data in practice
Imagine two software companies founded the same year. One has a clear website, an active help center, coverage in trade publications, and mentions in forum discussions and comparison articles. The other has a thin site and almost no third-party coverage.
When a model trained on public web data is asked to name tools in their category, the first company has many consistent mentions for the model to learn from. The second has almost none. Neither company can submit itself into a training set. The difference comes from how much accurate, crawlable information about each brand existed on the open web. This is a hypothetical example, but it reflects how pretraining works.
How to influence what AI models learn about you
You can't edit a model's training data or know exactly what it includes. You can make accurate information about your business more common and easier to crawl.
- Publish clear, factual content. State what you do, who you serve, and your key facts in plain text on crawlable pages.
- Earn third-party mentions. Coverage in industry publications, directories, reviews and forums adds independent references to your brand.
- Keep information consistent. Use the same company name, description and details everywhere to avoid conflicting signals.
- Decide on training crawlers deliberately. Blocking GPTBot, ClaudeBot or Google-Extended in robots.txt opts your future content out of training for those companies. That may protect your content, but it also means future models may learn less about you from your own site.
- Don't neglect live retrieval. Since training data has a cutoff, make sure AI search bots can crawl your site so assistants can find current information.
Common misconceptions about training data
- "Blocking GPTBot removes me from ChatGPT." OpenAI uses GPTBot for training and a separate bot, OAI-SearchBot, for ChatGPT search. Blocking one doesn't block the other.
- "Models remember every page they saw." Models learn patterns, not a searchable copy of the web. Rarely mentioned facts are often learned poorly or not at all.
- "New content shows up in the model right away." Training happens periodically. New content reaches a model's memory only when a future version is trained.
How Butter approaches training data
There's no service that puts a brand directly into an AI model's training data, and we don't claim to offer one. What we do is improve the public footprint that models learn from and that AI search retrieves. In our GEO plan, that means one AI-crawlable content citation and backlink each month, knowledge graph submissions, and schema markup guidance, plus monthly prompt testing to see how AI tools describe you. We also walk you through the trade-offs of allowing or blocking training crawlers, so it's a deliberate choice.
Frequently asked questions
Can I find out if my website is in an AI model's training data?
Generally not with certainty. Most AI companies describe their data sources only in broad terms. Checking your server logs for training crawlers like GPTBot or ClaudeBot shows whether they visited, not whether your content was used.
How do I opt out of AI training?
Major AI companies document robots.txt user agents for this, such as GPTBot for OpenAI, ClaudeBot for Anthropic, and the Google-Extended token for Gemini. Opting out affects future collection, not models already trained.
Is training data the same as the data AI search uses?
No. Training data is what a model learned from before release. AI search retrieves current web pages at the time of a question, often through separate crawlers and indexes.