Synthetic Training Data
Artificially generated data used to train or fine-tune AI models.
Definition
Synthetic training data is artificially generated data used to train or improve AI models. Rather than collecting and labeling real-world data, synthetic approaches use AI or simulation to create training examples, often at lower cost and with more control over edge cases.
Synthetic data can augment limited real datasets, create examples for rare scenarios, and avoid privacy concerns of using actual user data. However, quality depends on how well synthetic examples represent real-world distributions.
Why It Matters
High-quality training data is often the bottleneck for AI development. Synthetic data can accelerate development, reduce costs, and enable training for scenarios where real data is scarce or sensitive.
For AI teams, synthetic data generation is becoming a core capability for efficient model development.
Examples in Practice
A fraud detection model is trained on synthetic fraudulent transactions since real fraud examples are rare and sensitive.
A computer vision system generates millions of synthetic product images to train detection without expensive photography.
A conversational AI uses synthetic dialogues to cover edge cases that rarely appear in real customer conversations.