Zero-shot prompting
Also called zero-shot learning, zero-shot
Asking an AI model to do a task with instructions only, without showing it any examples.
Zero-shot prompting means giving a model a task description and nothing else: no worked examples of inputs and correct outputs. "Translate this into Spanish" or "Classify this review as positive or negative" are zero-shot prompts. The model relies entirely on what it learned in training and on how clearly you describe the task.
Modern models handle most everyday tasks well zero-shot because instruction tuning trained them on huge numbers of instruction-and-answer pairs. For common tasks like summarising, translating or rewriting, a clear zero-shot prompt is usually the fastest place to start, and often the only prompt you need.
Zero-shot works less well when the task has a specific format, an unusual style, or a judgement call that is hard to describe. Labelling support tickets with your company's own categories, or writing in a particular brand voice, often needs examples. That is when you move to few-shot prompting, adding two or three examples that show the exact pattern you want.
Example
"Rewrite this paragraph in plain English for a 12-year-old reader. Keep all the facts." works zero-shot because plain-English rewriting is a familiar task and the constraints are clear.
Common mistake
Jumping to examples before trying clear instructions. Examples add length and can make the model copy surface features, like sentence length or topic, that you did not intend. Start zero-shot with precise instructions, and only add examples when you see a specific failure they would fix.