PromptSharpGlossary › Few-Shot Prompting

What is Few-Shot Prompting?

Few-shot prompting gives the model a few worked examples of the task so it copies the pattern for your real input.

Few-shot prompting means including a small number of input-output examples inside the prompt before your actual request. The model infers the pattern from the examples and applies it — no retraining required. It is the single highest-leverage way to lock in a consistent format, tone, or labeling scheme.

Use it when zero-shot output drifts: the model gets the idea but formats or classifies inconsistently. Two to five clean examples usually fix it. The examples should look exactly like what you want back.

Example

Classify each ticket as BUG, FEATURE, or QUESTION.
Ticket: 'App crashes on login' -> BUG
Ticket: 'Add dark mode' -> FEATURE
Ticket: 'How do I export?' -> QUESTION
Ticket: 'Payment failed twice' ->

Frequently asked

What is the difference between zero-shot and few-shot?

Zero-shot gives the model only the instruction with no examples. Few-shot adds a handful of worked examples so the model copies the exact pattern — usually more consistent for formatting and classification tasks.

How many examples is 'few'?

Typically two to five. One example (one-shot) helps; beyond about five you hit diminishing returns and burn context window. Quality and consistency of the examples matters more than quantity.

Related terms

Put this into practice

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Reality guardrail: definitions here are practical, not academic. AI models predict likely text, not verified truth — always check names, numbers, and citations against a trusted source before you rely on an answer.

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