Zero-shot Prompting
Zero-shot prompting asks a model to perform a task from an instruction alone, without any worked examples in the prompt.
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.
Sources
- Brown et al. (2020). Language Models are Few-Shot Learners.
- Wei et al. (2021). Finetuned Language Models Are Zero-Shot Learners.
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/
BibTeX
@misc{protologue_zero_shot_prompting,
title = {Zero-shot Prompting},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0008},
url = {https://protologue.com/t/zero-shot-prompting/}
}