A synthetic data generation using large language models (LLMs) enables the creation of high-quality datasets without manual collection, cleaning, and annotation. This process leverages an LLM to generate artificial data that can be used to train, fine-tune, and evaluate LLMs themselves. Synthetic data generation involves creating synthetic queries, evolving them multiple times using various methods such as self-improvement or distillation, and combining the evolved queries with context to form a final dataset. Data evolution is crucial for ensuring the quality, comprehensiveness, complexity, and diversity of the dataset. A step-by-step guide is provided on how to use LLMs to generate synthetic datasets using DeepEval, an all-in-one platform for evaluating and testing LLM applications.