Few-shot Prompting

Overview

Few-shot prompting is a technique in which a model is trained to perform a specific task with limited or few training examples, also called shots, typically in the range of 1–10. In few-shot learning, the model is fine-tuned on a smaller dataset of examples, often referred to as a support set, to learn the underlying patterns and rules of the task. The model is then tested on a separate dataset, called the query set, to evaluate its performance. Few-shot prompting can be helpful when the data available for training is limited or costly to obtain.

Few-shot prompting facilitates contextual learning by presenting examples within the prompt to guide the model toward improved performance. These examples act as conditioning for subsequent instances where we want the model to produce the desired response.