Fundamentals

One-Shot Learning

One-shot learning provides exactly one example before asking the model to perform a task. This minimal demonstration can significantly improve performance compared to zero-shot, especially for structured tasks or unusual formats. One-shot is useful when you want to show the model a specific output format or style without needing multiple examples. It's a quick way to boost performance while keeping prompts short and costs low.