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, including more than $875 billion in real small-business transactions, enabling it to recognize business-specific patterns and automate bookkeeping and transaction categorization with high accuracy.

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