Deliberate Data Generation
Manually creating or collecting additional labeled examples that fill specific
gaps in your dataset. The most controlled option, but also the most 
resource-intensive.
Data Augmentation
Using your existing data to create additional variations. In imaging applications,
this might mean rotating, scaling, or transforming images. The underlying
information is real; the new examples are derived from it.
AI-Generated Data
Using generative models to produce new synthetic examples from scratch.
Powerful, but requires careful validation — generated data can look realistic
while failing to represent the true distribution of real-world inputs.
A Word of Caution
Synthetic data is a great tool for bootstrapping, but may not be a substitute for all purposes.
A model trained predominantly on synthetic data should always be fine-tuned and validated
against real-world examples as they become available. 
Keep a human in the loop throughout. Synthetic generation can introduce subtle biases that
aren't immediately obvious.
3 WAYS
to Fill Your Data Gaps
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