Chinmay Prakash

LLMs today promise endless text generation, but creating high-fidelity synthetic text data that actually reflects complex business logic remains an engineering challenge. This talk moves beyond basic "prompt and pray" techniques to address the various nuances of creating instruction datasets useful for knowledge distillation, domain adaptation, and reinforcement learning (RL) workflows. We will examine why direct generation often causes datasets to regress to the mean, producing repetitive, safe content that lacks the messy edge cases required for robust training. To solve this, we suggest a systematic, algorithmic approach that treats data generation as an engineering problem. We will discuss how to decompose pipelines into iterative batches to programmatically inject real-world variations. We also conclude with a strategic checklist to evaluate if synthetic data is truly well-suited to your enterprise problem.