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AI glossary

Training Data

Training data is the collection of examples an AI model learns from during training - the information it studies to learn how to perform a task.

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In this guide

  1. 1What Training Data means
  2. 2Why Training Data matters

What Training Data means

An AI model starts out knowing nothing. Training data is the material it studies to learn patterns - and the quality, quantity, and variety of that data strongly shape how well the finished model performs.

If you wanted an AI to recognize dog breeds, the training data would be many labeled photos of dogs. A model trained only on pictures of large dogs would struggle with small breeds, because its training data did not include them.

Why Training Data matters

Training data explains both why AI is powerful and why it sometimes gets things wrong. Understanding it helps you set realistic expectations for any AI tool.

Data quality directly affects how reliable an AI tool is
Gaps or bias in training data lead to gaps or bias in results
It explains why AI models have a knowledge cut-off date
Knowing this helps you judge when to trust an AI's output

Frequently asked questions

An AI model only knows what was in its training data. If that data was collected up to a certain date, the model will not know about events or facts that came after it.

More AI terms

Fine-TuningFurther training an existing AI model on extra, focused examples so it performs better on a specific task.Foundation ModelA large, general-purpose AI model trained on broad data that can be adapted for many different tasks.TransformerA type of neural network design that powers modern language models and handles sequences like text well.InferenceThe stage where a trained AI model is actually used to produce an answer or result from new input.GPTA family of large language models - GPT stands for Generative Pre-trained Transformer.Supervised LearningA machine learning method where a model learns from examples that are already labeled with the correct answer.

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