Instruction-tuning is a training technique that teaches AI models to follow human instructions effectively. Rather than just learning to predict the next token in a sequence, instruction-tuned models learn what humans actually expect when they give a directive — answer clearly, summarize concisely, format the output a specific way, take action when asked.
This makes models significantly more useful in practice than base models that have only learned to continue text. Instruction-tuning is largely responsible for the shift from "impressive but hard to use" models to AI systems that reliably do what you ask them to do.
