This chapter establishes the groundwork for understanding synthetic data within the context of Large Language Models (LLMs). The development of capable LLMs is heavily dependent on extensive and diverse datasets. Acquiring such data can be difficult due to availability, cost, or privacy concerns. Synthetic data provides a way to address these data requirements by artificially generating information.

Throughout this chapter, you will learn to define synthetic data and its characteristics, especially as it pertains to LLMs. We will examine the significant data needs of current LLMs and then compare synthetic data sources with authentic ones, weighing their respective advantages and limitations. You'll be introduced to a range of methods for generating synthetic data and see how this data fits into the pretraining and fine-tuning processes. Furthermore, we will discuss the attributes that contribute to high-utility synthetic data. The chapter concludes with guidance on the initial setup for projects focused on synthetic data generation.