Few-shot and zero-shot learning are machine learning paradigms that enable models to generalize to new tasks or classes with minimal labeled examples—ranging from none (zero-shot) to a small handful (few-shot). These approaches are foundational to in-context learning in large language models and meta-learning in computer vision, where models learn to adapt quickly by transferring knowledge from prior experience rather than requiring extensive task-specific training. The key challenge is to design representations, prompting strategies, and meta-learning algorithms that maximize generalization from extremely limited supervision, making these techniques essential for real-world applications where labeled data is scarce, expensive, or rapidly changing. Understanding the nuances between demonstration selection, calibration methods, and architectural choices directly impacts whether a model performs near state-of-the-art or random-guess accuracy on new tasks.