few-shot-prompting

Create structured few-shot prompts with examples for consistent LLM outputs.

1|Updated Feb 11, 2026

Search Tags:#templates #chain-of-thought #llm #few-shot #prompting #json-output

Ready to run

npx skills add https://github.com/xiaoshuangLi/aibo --skill few-shot-prompting

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: few-shot-prompting
Source: https://github.com/xiaoshuangLi/aibo/tree/main/skills/few-shot-prompting
Command: npx skills add https://github.com/xiaoshuangLi/aibo --skill few-shot-prompting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Few-shot prompting dramatically improves LLM output quality and consistency by providing 2-8 examples of the desired input→output behavior, enabling rapid task teaching without fine-tuning.

Core Features & Use Cases

Basic Few-Shot Template: provide a minimal template to shape task behavior.
JSON Output Few-Shot: enforce structured outputs like JSON.
Code Transformation Few-Shot: help convert code styles or patterns.
Example Selection Guidelines: guidance on choosing diverse, edge-case examples.
Chain-of-Thought Few-Shot: includes reasoning traces in examples for complex tasks.
Evaluation & Guardrails: checks for correctness and consistency.

Quick Start

Create a prompt with 3-5 representative examples and a clearly defined output format, then test on real inputs.

Frequently Asked Questions about few-shot-prompting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I improve LLM output consistency for new task formats without fine-tuning?▼

Use few-shot prompting to improve LLM output consistency by providing 2-8 structured input-output examples, enabling rapid task teaching without fine-tuning.

How do I create few-shot prompts that enforce JSON output from large language models?▼

Create JSON output few-shot prompts by providing 3-5 representative examples with clearly defined structured JSON formats to enforce consistent outputs from LLMs.

What is the best way to guide LLMs through complex reasoning tasks like code transformation?▼

Chain-of-thought few-shot prompting guides complex reasoning and code transformation tasks by including step-by-step reasoning traces within the provided examples.

How many examples should I include in a few-shot prompt for data extraction?▼

Include 3-5 representative examples in a few-shot prompt for data extraction, ensuring diverse selections that cover edge cases for reliable transformations.

When should I not use few-shot prompting for LLM tasks?▼

Avoid few-shot prompting when tasks are highly ambiguous or token limits are critical, as providing 2-8 examples increases prompt length and consumes context window capacity.