Zero-Shot

Zero-Shot Learning refers to a model's ability to perform tasks it wasn't explicitly trained on, using only natural language instructions without any examples. This capability distinguishes modern large language models from earlier AI systems that required task-specific training data.

In practice, zero-shot prompting means describing what you want the model to do and getting reasonable results without providing examples. For instance, asking a model to "classify this review as positive or negative" without showing it any classified examples. More capable models generally have better zero-shot performance.

Zero-shot vs few-shot is a key decision in prompt engineering. Zero-shot is faster and simpler, but few-shot often produces more consistent, higher-quality results. Engineers should test both approaches: start with zero-shot for simplicity, and add examples if results are inconsistent or need to follow specific formats.