Few-shot learning is a type of machine learning approach where a model is trained to generalize from only a small number of labeled examples. Unlike traditional deep learning, which often requires massive datasets to achieve high accuracy, few-shot learning aims to mimic human-like learning by adapting to new tasks with minimal data. This capability is especially useful in scenarios where labeled data is scarce, costly, or time-consuming to collect, such as rare disease detection, niche product classification, or personalized AI applications.
Few-shot learning is closely associated with large language models (LLMs) and foundation models, which are pre-trained on vast amounts of data and can be “prompted” with a few examples to perform a new task without retraining. It also has applications in computer vision, robotics, and recommendation systems, enabling faster development cycles and broader AI accessibility across domains.