Training Data
The dataset used to teach an AI model to recognize patterns, make predictions, or generate content.
Definition
Training data is the collection of examples, text, images, or other information used to teach an AI model how to perform its intended task. The model learns patterns, relationships, and rules from this data during the training process. The quality, quantity, diversity, and representativeness of training data fundamentally determine the model's capabilities and limitations.
Training data can be labeled (annotated with correct answers for supervised learning), unlabeled (raw data for unsupervised learning), or a combination. Large language models are trained on vast text corpora, while image models learn from millions of labeled photographs.
Why It Matters
Training data is the single most important factor determining an AI model's quality and behavior. The adage "garbage in, garbage out" applies directly — biased, incomplete, or low-quality training data produces biased, limited, or unreliable AI outputs.
For organizations using AI, understanding training data helps evaluate tools, identify potential bias, and assess reliability. If a model was trained primarily on data from one demographic or industry, its performance may degrade when applied to others.
Examples in Practice
A medical AI system trained primarily on data from one hospital chain produces inaccurate results when deployed at facilities with different patient demographics, demonstrating why training data diversity matters.
A company creates a competitive advantage by curating high-quality, proprietary training data from their industry, fine-tuning a model that outperforms general-purpose alternatives.
An AI ethics audit reveals that a hiring tool's training data over-represented candidates from elite universities, creating a systematic bias that excluded qualified candidates from non-traditional backgrounds.