Zero-shot learning lets an AI handle tasks it has not been explicitly trained on by understanding the instructions given in the prompt. Without any examples, the model applies its general language understanding to figure out what is being asked and how to respond.
Zero-shot learning is how general-purpose models handle novel tasks without requiring a retraining cycle for every new use case. It is what makes modern LLMs so flexible in practice. However, performance typically improves meaningfully when at least a few examples are provided — which is why few-shot prompting is often preferred when examples are easy to supply. Zero-shot is the floor; few-shot is usually the better starting point for production use cases.
