# Zero-shot Prompting

> Zero-shot prompting asks a model to perform a task from an instruction alone, without any worked examples in the prompt.

- Identifier: PTL-0008
- Category: Foundations
- Canonical URL: https://protologue.com/t/zero-shot-prompting/

## Description

Zero-shot performance improved dramatically with instruction tuning and preference training, which taught models to follow natural-language task descriptions. It is the default for most modern chat use, with examples added only when the format or judgment required is hard to describe.

## Related terms

- [Few-shot Prompting](https://protologue.com/t/few-shot-prompting/)
- [Instruction Tuning](https://protologue.com/t/instruction-tuning/)
- [Zero-shot Chain-of-Thought](https://protologue.com/t/zero-shot-chain-of-thought/)

## Sources

- Brown et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165
- Wei et al. (2021). Finetuned Language Models Are Zero-Shot Learners. https://arxiv.org/abs/2109.01652

## Cite this entry

Protologue. (2026). Zero-shot Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0008). https://protologue.com/t/zero-shot-prompting/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)