Creating high-quality datasets is a cornerstone of machine learning. However, traditional methods can be costly and time-consuming. Researchers have explored using Large Language Models (LLMs) for synthetic data generation, but existing techniques face challenges in ensuring diversity, accuracy, and controllability. Enter UniGen, a unified framework leveraging LLMs to produce top-tier textual datasets. UniGen tackles the limitations of previous methods head-on. To enhance diversity, it employs attribute-guided generation, where the LLM is given specific attributes (like "economics" or "sports") to guide the creation of varied data. It also uses group checking to filter out highly similar items, ensuring a diverse dataset. For accuracy, UniGen incorporates a code-based mathematical assessment to verify the correctness of labels in math-related datasets. Additionally, it uses Retrieval-Augmented Generation (RAG) to cross-check generated statements against a knowledge base like Wikipedia, enhancing factual accuracy. UniGen isn’t just about improving quality; it also empowers users with control. The framework allows for user-specified constraints, giving developers fine-grained control over various aspects of the generated data, such as length, topic, or specific formatting requirements. Experiments with UniGen show promising results, outperforming prior techniques on popular benchmark datasets. It has exciting applications, including dynamic benchmarking, where UniGen creates ever-evolving challenges for LLMs, helping to more accurately assess their true capabilities. It also aids in data augmentation, boosting LLM performance by generating additional training data. While still early, UniGen offers a compelling glimpse into the future of dataset creation. By combining the power of LLMs with clever generation and validation techniques, it offers a robust solution to the challenges of building high-quality datasets, paving the way for more capable and reliable AI models.