What is Zero-shot vs Few-shot?
Zero-shot means asking an AI to do a task with no examples; few-shot means including a handful of examples so it copies the pattern.
Zero-shot and few-shot describe how much demonstration you give a model before it performs a task. Zero-shot is the default use of a chatbot: you describe the job and the model attempts it from general knowledge. Few-shot means you include two to five worked examples — input and desired output pairs — inside the prompt, so the model infers the exact format, tone, and level of detail you want. Few-shot is the cheapest way to make output consistent, and it usually beats a longer written description of your rules. It is especially effective for classification, data extraction, structured formatting, and any task where your team already has good past examples. The trade-off is that examples consume tokens in every request and take up context window space. If you need the same behaviour thousands of times a day, fine-tuning eventually becomes cheaper than paying for the same examples on every call. For most people comparing AI tools, few-shot prompting closes most of the quality gap.
Also known as
- zero shot
- few shot prompting
- in-context learning
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Related terms
- Prompt Engineering — Prompt engineering is the practice of writing instructions that reliably get the output you want from an AI tool.
- Fine-tuning — Fine-tuning is retraining an existing AI model on your own examples so it adopts your style, format, or domain knowledge.
- Large Language Model (LLM) — An LLM is an AI model trained on huge amounts of text that predicts language well enough to write, summarise, translate, and answer questions.
- Token (AI) — A token is the small chunk of text — roughly three-quarters of a word — that AI models read and generate, and that most AI pricing is based on.
Category: AI Concepts