Instruction Tuning and Alignment

Instruction tuning is the process of fine-tuning AI models to better follow human instructions and align with human preferences. This process stems from the same fundamental concept as prompt engineering—crafting clear instructions that produce desired outcomes.

Models like ChatGPT, Claude, and Llama 2 have undergone extensive instruction tuning where they learn from examples of instructions paired with high-quality responses. This training helps models understand implicit expectations in human instructions and produce more helpful, harmless, and honest outputs.

Reinforcement Learning from Human Feedback (RLHF) further refines models by using human evaluators to rank different possible responses, creating a reward signal that shapes the model's behavior toward preferred outputs.

Understanding how models are instruction-tuned can help users craft prompts that work with—rather than against—the model's training, resulting in more effective interactions.