Instruction tuning — fine-tuning a base model on diverse instruction-response pairs — transforms a next-token predictor into a system that reliably follows user intent expressed in natural language. From a prompting perspective, this is what makes techniques like few-shot prompting, chain-of-thought, and structured output requests work consistently rather than requiring careful prompt hacking around a base model's completion tendencies.