Few-shot prompting

Including one to five worked examples in the prompt so the model copies their format, tone and level of detail.

Last updated: November 9, 2026 · Markdown version

How to apply it in prompts

Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.

Strict JSON Models Packs

What it means

Definition: you show the model what a good input-and-output pair looks like before giving it the real input. The model infers the pattern from the examples instead of from a description.
Why it matters: examples fix output structure and style far more reliably than adjectives do, which makes few-shot the usual fix for inconsistent formatting.
How to apply it: start with one or two small, representative examples, label them clearly (Input / Output) and end the prompt with the real input. Add an example only when the model keeps getting one case wrong.
Common mistakes: examples that are all alike (the model overfits to them), examples that contradict your written rules, and long examples that burn tokens. Some search-based models latch onto the topic of an example, so describe the structure there instead.

Copy-paste example

Convert each input to JSON with the keys name (string) and age (number).

Example 1
Input: "Name: Ana, Age: 31"
Output: {"name":"Ana","age":31}

Example 2
Input: "Luis, 27 years old"
Output: {"name":"Luis","age":27}

Now do this one.
Input:
"""[paste]"""
Output ONLY the JSON.

Opens the analyzer with the prompt prefilled.

Open in Promptea

FAQ

How many examples should I use?

Start with one or two. Add more only if the model is still inconsistent, and make the new ones cover a different case, not a repeat of the first.

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

Zero-shot gives only the instruction; few-shot adds worked examples. Use zero-shot for simple, well-defined tasks and few-shot when format or edge cases matter.