Having established methods for generating synthetic data and its application in Large Language Model pretraining and fine-tuning, this chapter moves to more sophisticated techniques for data enhancement and process refinement.

Here, you will learn about augmenting data directly within embedding spaces and using synthetic information to construct structured learning paths, often referred to as curriculum learning. We will also cover the generation of synthetic preference data, an essential component for alignment methods such as Reinforcement Learning from AI Feedback (RLAIF). Further, this chapter details the development of automated pipelines for filtering and cleansing synthetic datasets, procedures for automated quality assurance, and strategies for the iterative improvement of data generation workflows. A practical exercise will guide you through implementing a script for data filtering.