Zero-Shot Prompting: Definition and Examples

Zero-shot prompting gives a direct instruction to an AI model without any examples. The model relies on its training knowledge.

Full definition

Zero-shot is the simplest form of interaction with an AI. You ask your question and the model answers directly. It is the daily use of ChatGPT or Claude.

This approach works thanks to the mass of training data. For common tasks (translation, summarization, Q&A), zero-shot is enough.

Its limitations: very specific formats or particular styles. In these cases, few-shot helps.

To maximize zero-shot: specify the role, output format, constraints, and context. A good zero-shot prompt can rival few-shot.

Concrete examples

Simple classification

Classify this email: 'spam', 'promotion' or 'important':

"You won an iPhone! Click here."

Category:

Text summarization

Summarize this text into 3 concise bullet points:

[YOUR_TEXT]

Rich translation

Translate this marketing text FR→EN US. Keep the dynamic tone, adapt cultural references.

Text: [YOUR_TEXT]

Practical usage

Always start in zero-shot. If results are not satisfactory, enrich the instruction. If that is not enough, move to few-shot.

Related concepts

Few-shotOne-shotInstruction tuningPrompt engineering

FAQ

When is zero-shot enough?

Standard tasks: translation, summarization, Q&A, writing, classification, correction. If it's intuitive, zero-shot will work.

How to improve a zero-shot prompt?

1) Role, 2) Output format, 3) Constraints, 4) Chain-of-thought.

Zero-shot vs few-shot?

For well-understood tasks, zero-shot can be just as good. Few-shot excels for precise formats. Zero-shot consumes fewer tokens.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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