With a foundational understanding of synthetic data's role from the previous chapter, we now turn to the 'how': the specific techniques used to generate synthetic text. This chapter provides a practical overview of these methods.

You will learn to:

• Implement algorithmic and rule-based systems for text creation.
• Use back-translation as a strategy for data expansion.
• Employ paraphrasing models to introduce diversity into your text datasets.
• Utilize Large Language Models (LLMs) directly for generating new data samples, with a focus on effective prompt design to control the output.
• Apply data masking and perturbation techniques to produce varied and privacy-respecting data.

The chapter includes a hands-on exercise where you'll use an LLM API to generate text, putting these techniques into practice. By progressing through these sections, you will build a toolkit for producing synthetic text tailored to various LLM development needs.