Training data is the collection of examples used to teach an AI model to recognize patterns, make predictions, or generate output.
The quality, diversity, and representativeness of training data directly affects what an AI model learns — and what it gets wrong. A model trained on biased or incomplete data will reflect those limitations in its outputs.
Training data for large language models typically includes vast amounts of text from the internet, books, and other sources. This means the model may reflect errors, biases, and outdated information present in that data.
Understanding that AI outputs are shaped by training data helps you evaluate those outputs critically. If the training data was limited, biased, or outdated, the model's outputs may be too.