Prompt Engineering

Prompt Engineering

Quick Start

import openai

client = openai.OpenAI()

# Basic prompt
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an expert engineer."},
        {"role": "user", "content": "Explain mooring systems."}
    ]
)

print(response.choices[0].message.content)

When to Use This Skill

USE when:

• Designing prompts from scratch for any use case
• Learning core principles applicable across all LLMs
• Need portable patterns not tied to specific frameworks
• Building simple LLM integrations without heavy dependencies
• Optimizing existing prompts for better results
• Creating reusable prompt templates for teams
• Debugging underperforming LLM applications
• Teaching prompt engineering to others

DON'T USE when:

• Need framework-specific features (use LangChain/DSPy)
• Require programmatic optimization (use DSPy)
• Building production RAG systems (use LangChain)
• Need conversation memory management (use frameworks)

Prerequisites

# OpenAI
pip install openai>=1.0.0
export OPENAI_API_KEY="sk-..."

# Anthropic
pip install anthropic>=0.5.0
export ANTHROPIC_API_KEY="sk-ant-..."

# Azure OpenAI
pip install openai>=1.0.0
export AZURE_OPENAI_ENDPOINT="https://..."
export AZURE_OPENAI_KEY="..."

# Optional: For testing prompts
pip install pytest promptfoo

Resources

• OpenAI Prompt Engineering Guide: https://platform.openai.com/docs/guides/prompt-engineering
• Anthropic Prompt Engineering: https://docs.anthropic.com/Codex/docs/prompt-engineering
• Prompt Engineering Guide: https://www.promptingguide.ai/
• Learn Prompting: https://learnprompting.org/

Version History

• 1.0.0 (2026-01-17): Initial release with comprehensive prompting patterns

Sub-Skills

• 1. Zero-Shot Prompting (+1)
• 3. Chain-of-Thought Prompting
• 1. Be Specific and Clear (+1)
• Inconsistent Outputs (+2)

Sub-Skills

• Anatomy of a Prompt (+1)
• Understanding
• Approach
• Calculation
• Verification
• 4. System Prompt Design
• Expertise
• Communication Style
• Constraints
• Your Task
• Response Format
• Guidelines
• Your Task
• Response Format
• 5. Persona Design
• Background
• Notable Experience
• Communication Style
• Approach
• Communication Adaptation
• 6. Structured Output (+2)
• Example 1: Multi-Stage Document Processor (+1)
• Summary
• Findings
• Recommendations
• OpenAI Integration (+1)
• Input
• Task
• Output Format
• 3. Provide Context (+1)