What is an Instruction Hierarchy in prompt engineering?

It is a structural best practice that organizes information from global context down to specific input and output formats, helping the model prioritize instructions correctly.

How does Chain-of-Thought improve AI performance?

Chain-of-Thought forces the model to reason step-by-step before providing a final answer, which significantly reduces errors in complex logic or mathematical problems.

When should I use Few-Shot Learning instead of simple instructions?

Use Few-Shot Learning when the desired output format is difficult to describe but easy to show, or when the model needs to learn specific stylistic patterns through examples.

How can I reduce token usage in complex prompts?

This skill teaches prompt optimization techniques that balance the number of examples against task complexity to find the most cost-efficient instruction set.

Prompt Engineering Architect

Name: Prompt Engineering Architect
Author: claudiodearaujo

byclaudiodearaujo

0•

Learning & Documentation

Optimizes Large Language Model performance through advanced prompting patterns, systematic refinement, and best practice implementation.

This skill acts as an expert consultant for developers looking to maximize the reliability and controllability of Claude and other AI models. It provides a structured framework for implementing advanced techniques like Few-Shot Learning for consistent outputs and Chain-of-Thought prompting for complex analytical reasoning. By following the included architectural blueprints for prompt optimization, template systems, and system prompt design, users can reduce hallucinations, improve response accuracy by up to 50%, and create production-ready AI interactions that remain stable across diverse inputs.

Key Features

01Modular template systems for reusable and scalable prompt structures

02Few-Shot Learning patterns for consistent formatting and edge-case handling

03Chain-of-Thought reasoning frameworks for multi-step logical tasks

04Advanced instruction hierarchy and error recovery patterns

050 GitHub stars

06Systematic prompt optimization and iterative A/B testing strategies

Use Cases

01Reducing token costs through optimized prompt engineering and refinement

02Improving the consistency and accuracy of automated AI agent behaviors

03Developing complex system prompts for specialized production-grade applications

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