What is training data?

Training data is the collection of examples an AI model learns from to recognize patterns, make predictions, and perform tasks. The quality and relevance of that data directly affect how accurately an AI model performs.

The quality of an AI model depends on the quality of its training data. Models trained on relevant, domain-specific data generally perform better than models trained only on broad, general-purpose data.

Example: Digits' Agentic General Ledger™ (AGL®) is trained on accounting-specific data — 180 million transactions representing nearly $1 trillion in real financial activity — enabling it to recognize business-specific patterns rather than relying on general world knowledge.

Related terms: Tiered Intelligence, Agentic General Ledger™ (AGL®), Machine Learning, AI-Native Accounting, Model layer cake