When should I use few-shot learning instead of instructions?

Few-shot learning is most effective when you need consistent formatting or specific reasoning patterns that are easier to show through 2-5 examples than to explain via rules.

What is the recommended structure for a high-quality prompt?

The skill suggests an 'Instruction Hierarchy' consisting of System Context, followed by Task Instructions, Examples, Input Data, and finally the Output Format.

What is the benefit of Chain-of-Thought prompting?

It significantly improves accuracy on analytical and multi-step logic tasks by requesting step-by-step reasoning before the model provides a final answer.

How does this skill help with prompt cost optimization?

It teaches optimization techniques to balance example count with token usage and utilizes system prompts to keep conversation turns efficient.

Can I use these patterns for production-grade applications?

Yes, the skill covers professional practices like A/B testing variations, monitoring performance metrics, and treating prompts as version-controlled code.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: claudiodearaujo

byclaudiodearaujo

학습 및 문서화

Master advanced prompting techniques to enhance Claude's reasoning, consistency, and output quality through proven engineering patterns.

소개

This skill equips Claude with specialized knowledge in prompt engineering patterns, enabling users to systematically improve agent behavior and model outputs. It provides actionable guidance on few-shot learning, chain-of-thought reasoning, and modular template design to maximize the performance of large language models. Whether you are debugging complex logic, designing system prompts, or optimizing for token efficiency, this skill serves as an expert consultant for building reliable AI-driven workflows and production-grade prompts.

주요 기능

Leverages Chain-of-Thought reasoning to solve complex multi-step analytical problems
Optimizes prompt structures through systematic iteration and performance measurement
0 GitHub stars
Implements Few-Shot learning patterns for consistent formatting and edge-case handling
Designs high-performance system prompts to manage global behavior and constraints
Develops reusable template systems for modular and scalable AI interactions

사용 사례

Debugging and refining inconsistent or unreliable AI agent behaviors
Creating standardized, production-ready output formats for AI applications
Building complex reasoning chains for technical or mathematical tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front prompt-engineering

For use in Claude.ai and ChatGPT

Download Skill

GitHub

소개