Augmenting with Synthetic Data

Having explored both comprehensive and parameter-efficient methods for fine-tuning, we now address a common bottleneck that underlies all adaptation techniques: the availability of data. Sometimes, the high-quality, domain-specific data needed for a perfect fine-tuning process is simply unavailable. You might lack sufficient examples for a niche task, need to cover rare instruction types, or face privacy constraints that prevent the use of real user data. In such situations, we rely on a powerful and increasingly crucial technique: Synthetic Data Generation. This chapter will explore how to create data artificially, using other AI models as a tool, allowing us to augment, bootstrap, or even replace traditional datasets to achieve our fine-tuning goals.