# Few-shot Prompting vs Zero-shot Prompting

> Zero-shot prompting gives a model only an instruction. Few-shot prompting adds a small number of worked input-output examples so the model can infer the task and format from demonstrations, at the cost of a longer prompt and sensitivity to which examples are chosen and in what order.

- Canonical URL: https://protologue.com/compare/few-shot-vs-zero-shot-prompting/

## Few-shot Prompting

Few-shot prompting includes a small number of input-output examples in the prompt so the model can infer the task and the desired format from demonstrations.
Entry: https://protologue.com/t/few-shot-prompting/

## Zero-shot Prompting

Zero-shot prompting asks a model to perform a task from an instruction alone, without any worked examples in the prompt.
Entry: https://protologue.com/t/zero-shot-prompting/

## Differences

| | Few-shot Prompting | Zero-shot Prompting |
|---|---|---|
| Prompt contents | Instruction plus several examples | Instruction only |
| Strength | Conveys formats and judgments that are hard to describe | Short and simple |
| Weakness | Results depend on example choice, order and label balance | Ambiguous when the desired format is unusual |

## Which to use

- Add examples when the output format or the judgment required is hard to put into words.
- Start zero-shot with modern instruction-tuned models, and add examples only if outputs miss the mark.

## Sources

- Brown et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165
- 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 page

Protologue. (2026). Few-shot Prompting vs Zero-shot Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0). https://protologue.com/compare/few-shot-vs-zero-shot-prompting/

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