What is Training Data?

Training data is the massive corpus of text that large language models learn from, shaping their knowledge and how they discuss brands and topics.

Large language models are trained on billions of documents - web pages, books, articles, code repositories, and more. This training data forms the model's understanding of everything, including brands and products. Your presence in quality training data influences how AI systems describe and recommend your brand.

Why it matters

Training data matters because it's the foundation of AI knowledge. While web-connected AI can access current information, base model knowledge - which influences recommendations even in web-enabled scenarios - comes from training data. Brands that build strong presence in authoritative sources today are investing in AI visibility for years to come. Training data is the long game of AI visibility.

What people get wrong

• You can add content directly to training data. Training data is compiled by AI companies. You can only influence it by having presence in sources they include.
• Training data is constantly updated. Base model training happens periodically, typically every 6-18 months. Between updates, base knowledge is static.
• All online content becomes training data. AI companies filter and curate training data. Not all websites are included, and quality signals matter.

Related terms

LLM: LLMs learn everything they know from their training data.

Knowledge Cutoff: Knowledge cutoff is the date after which training data wasn't collected.

Hallucination: Gaps or inconsistencies in training data can contribute to hallucinations.

GEO: GEO strategies consider both current web presence and training data inclusion.

Understand your AI presence

Trakkr tracks how AI systems describe your brand, which reflects what they learned from training data. See how you're being described and identify gaps.