A practical example
A company may need to test an invoice extraction system but cannot upload customer invoices to a development environment. It can generate synthetic invoices with different vendors, currencies, missing fields, and formatting errors. The examples help exercise the pipeline, but they do not prove performance on real invoices until a protected evaluation set is used.
What synthetic data can help with
- Testing rare edge cases.
- Building a first prototype without exposing customer records.
- Balancing underrepresented labels in a dataset.
- Creating repeatable load and integration tests.
- Sharing examples across teams with less sensitive detail.
The risk
Generated data can be too clean, too similar, or biased toward the model that created it. It can also accidentally reproduce sensitive patterns. Keep a real, access-controlled evaluation set and compare distributions, failure modes, and subgroup behavior.