This research paper provides a comprehensive overview of techniques for generating synthetic data to improve the training and performance of Large Language Models (LLMs). The paper explores data augmentation, which enhances existing datasets, and data synthesis, which creates entirely new data samples. The authors categorize these techniques based on their use throughout the LLM lifecycle, including data preparation, pre-training, fine-tuning, instruction-tuning, and preference alignment. The paper also examines the limitations and challenges of these data generation methods and proposes future research directions to address these issues.