The practice of feeding a machine learning model a small amount of training data to adapt it to a specific task.
Few-Shot Learning draws on the pre-trained knowledge of a Foundation Model. Given a few accurate examples within the prompt (Prompt Engineering), the model picks up the desired pattern and format without the cost of full fine-tuning.
See also Foundation Model, Prompt Engineering, Fine-Tuning, Large Language Model and Model Inference.