Training Data
Training data is the body of text and other content an AI model learns from during training, shaping what it knows by default before any live retrieval is involved.
Training data is what the model learned from. It's the large corpus of web pages, books, and other text used to train a large language model, and it determines the model's default "knowledge" — what it can say before it retrieves anything live. Sources like Common Crawl and crawls by bots such as GPTBot feed this pipeline.
The AEO distinction to hold onto: being in the training data is not the same as being cited. Citations come from live retrieval, not from training. Where training does reward you is over the long run — genuinely original content, found nowhere else, is the kind of material that shapes how models discuss your topic and your brand as new models are trained.
Example. A widely-referenced original study may, over time, inform how models describe a field — even while today's citation of it comes from an engine retrieving the live page. Training shapes general knowledge; retrieval earns the link.
Relevant pillar
Related terms
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, and is the engine that writes the answers in AI search.
- Common CrawlCommon Crawl is a nonprofit that publishes a free, massive archive of crawled web pages, which has served as a foundational training dataset for many large language models.
- GPTBotGPTBot is OpenAI's web crawler that gathers content to train its models, identified by the GPTBot user-agent and controllable through your robots.txt file.