In-Context Learning

Use Few-Shot Prompting with Effective Examples

A prompt can explain a task with instructions, but sometimes examples communicate the desired behavior more precisely. Few-shot prompting places a small number of input-output demonstrations in the model’s context before the real input. This technique is useful when a task has a specific output format, subtle classification boundary, naming convention, or transformation rule that is difficult to describe completely in prose. The model is not retrained by these examples. Instead, it uses the demonstrations as part of the current context when generating the next response.