---
title: Zero-Shot Learning — AI Glossary
description: Zero-shot learning asks a model to perform a task from an instruction alone, with no worked examples provided. It relies entirely on knowledge acquired during pre-training and instruction tuning, making it the simplest and cheapest baseline before adding examples or heavier prompting scaffolds.
canonical: https://prompeteer.ai/glossary/zero-shot-learning
---

# Zero-Shot Learning

Zero-shot learning asks a model to perform a task from an instruction alone, with no worked examples provided. It relies entirely on knowledge acquired during pre-training and instruction tuning, making it the simplest and cheapest baseline before adding examples or heavier prompting scaffolds.

## Definition

In a zero-shot setting the prompt describes the task in plain language and the model responds directly, without demonstrations. Instruction-tuned models are trained to follow such directives, so for common, well-understood tasks a clear instruction is often sufficient.

Because nothing anchors the expected output format, zero-shot quality depends heavily on how precisely the instruction is phrased and how familiar the task is. It is the natural first thing to try; if results are inconsistent, adding a few examples (few-shot) or a reasoning directive typically helps.

## Sources

- [Brown et al. (2020), Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
- [Wei et al. (2022), Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652)

## Related terms

- [few-shot-learning](https://prompeteer.ai/glossary/few-shot-learning)
- [prompt-template](https://prompeteer.ai/glossary/prompt-template)
- [context-engineering](https://prompeteer.ai/glossary/context-engineering)