Ensuring the quality of synthetic data is paramount to achieving reliable fine-tuning results, and Dria employs a comprehensive, multi-step validation pipeline to meet this challenge. First, synthetic outputs are cross-validated across multiple AI models to compare and confirm consistency. Automated consistency checks are then applied to rapidly identify and filter out any low-quality or incoherent generations. This multi-layered approach delivers training data that is both reliable and effective for fine-tuning LLMs.