Few shot prompting is a widely used technique to enhance LLM performance that involves providing labeled examples embedded directly in the prompt help models generalize—especially for edge cases. It's a fast way to guide outputs without fine-tuning that helps a model generalize better and produce more accurate responses -- even with limited task-specific data. Learn more about proven prompt optimization techniques.