Data distillation is a technique that allows the transfer of knowledge from large, complex models, such as GPT-5 or Llama-3.3-70B, to smaller, more efficient models suitable for production environments. This process involves using the outputs of a large "teacher" model to create a curated dataset that a smaller "student" model can learn from, thereby retaining much of the teacher's capabilities while operating on standard hardware with faster response times. The technique addresses the challenge of deploying massive models that require expensive GPUs and have slow response times, by enabling smaller models to perform tasks with high accuracy and speed, essential for applications needing sub-second responses. Unlike knowledge distillation, which focuses on teaching a student model the probability distributions of the teacher's outputs, data distillation creates a dataset from the teacher’s decoded responses, allowing the student to learn from the teacher’s reasoning processes and final outputs. This approach is particularly relevant as language models grow increasingly large, necessitating efficient models that can achieve similar performance with fewer resources.