Zero-shot prompting: Zero-shot prompting is asking an AI model to do a task with instructions only, without showing it any examples of the output you want. It works well for common tasks the model has seen many times, such as summarizing or translating.
'Shot' is jargon for an example. A zero-shot prompt gives zero examples: 'Summarize this contract in five bullets' or 'Classify this support ticket as billing, technical, or account.' You are relying entirely on the model's general training to understand what a good answer looks like.
This is how most people prompt most of the time, and for familiar tasks it is fine. Modern models are strong zero-shot performers on summarizing, rewriting, explaining, translating, and answering questions. The better you describe the task, audience, and format, the better the result.
Zero-shot breaks down when your definition of 'good' is specific to you. If your team's meeting notes follow a particular structure, or your classification categories have subtle boundaries, the model will guess at your conventions and often guess wrong. That is the signal to switch to few-shot prompting and show it examples.
Example at work
A project manager types: 'Rewrite this status update for the executive steering committee. Lead with the decision needed, keep it under 100 words, and use no jargon.' No examples are provided. Because executive summaries are a common task, the result is usable after a light edit.
Why it matters
Knowing the term helps you diagnose a weak result. If a zero-shot prompt keeps missing your standard, the fix is usually not a longer description of what you want. It is one or two real examples of it.
Related terms
- Few-shot promptingFew-shot prompting is including a handful of examples of the input and output you want inside your prompt so the AI model can copy the pattern. It is the fastest way to get consistent formatting, tone, and judgment calls.
- PromptA prompt is the text (and sometimes files or images) you give an AI model to tell it what you want. A good prompt states the role the AI should play, the task, the relevant context, the output format, and any constraints.
- Prompt engineeringPrompt engineering is the practice of writing and refining instructions to get reliable, high-quality output from an AI model. It covers giving context, showing examples, specifying format, and iterating on what did not work.
- Chain-of-thought promptingChain-of-thought prompting asks an AI model to work through a problem step by step before giving its final answer. Showing its reasoning tends to improve accuracy on math, logic, and multi-step tasks and makes mistakes easier to spot.