# Zero-shot prompting

**Source:** https://promtable.com/glossary/zero-shot-prompting

> Zero-shot prompting asks the model to perform a task with no examples — only the instruction and the input.

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Zero-shot prompting asks the model to perform a task with no examples — only the instruction and the input.

Zero-shot prompting relies on the model's pretrained capability rather than in-prompt demonstrations. It is the default style for large frontier models (GPT-4 class and above) on common tasks. Zero-shot is cheaper (fewer prompt tokens) and easier to maintain, but its quality is highly sensitive to prompt wording. "Translate the following to French" works fine; "Convert this to French in a casual Marseille accent" usually needs few-shot examples. As a rule of thumb: try zero-shot first, add CoT or few-shot only when evals show measurable gain.

## When to use

- Standard tasks the frontier model has clearly seen in pretraining (translation, summarization).
- Latency- or cost-sensitive workloads.
- Prompts that are read by humans and need to stay short.

## Common mistakes

- Assuming zero-shot will hit production quality without evals.
- Skipping zero-shot and jumping straight to fine-tuning — usually overkill.

## Related terms

- [few-shot-prompting](https://promtable.com/glossary/few-shot-prompting)
- [chain-of-thought](https://promtable.com/glossary/chain-of-thought)
- [system-prompt](https://promtable.com/glossary/system-prompt)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/zero-shot-prompting
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