A comprehensive learning resource for mastering prompt engineering techniques specifically designed for software developers. This course provides structured tutorials, hands-on exercises, and real-world implementation examples to help you integrate AI assistants effectively into your development workflow.

Following the proven structure of AWS educational resources, this course is organized into three main sections:

Complete tutorials teaching prompt engineering from foundations to advanced integration:

• Module 1: Course introduction, environment setup, and prompt anatomy
• Module 2: Core techniques - clear instructions, personas, delimiters, reasoning
• Module 3: Software engineering applications - code quality, testing, debugging, APIs
• Module 4: Custom command integration for AI code assistants

Interactive exercises and assessments to reinforce learning:

• hands-on/: Guided practice activities for each module
• solutions/: Complete reference implementations with detailed explanations

Production-ready patterns and implementation examples:

• code-quality/: Refactoring, modernization, and quality improvement workflows
• debugging/: Incident investigation, root cause analysis, and resolution patterns
• api-integration/: Client generation, error handling, and robust integration patterns
• custom-commands/: Reusable command templates and team adoption strategies

1. 🎯 Start Here: 01-tutorials/module-01-foundations/ for environment setup
2. 📖 Learn: Progress through tutorials in order (modules 1-4)
3. 🛠️ Practice: Complete exercises in 02-exercises/hands-on/
4. 🎯 Apply: Implement patterns from 03-examples/ in real projects

• Python 3.8+ and package manager (uv recommended)
• IDE with notebook support (VS Code or Cursor)
• API Access to one of:
  • GitHub Copilot (recommended)
  • CircuIT APIs
  • OpenAI API key

Use uv to manage dependencies:

uv is a fast Python package installer and resolver.

Note: To use the Splunk hosted PyPi repository, use the following command:

brew upgrade okta-artifactory-login
okta-artifactory-login -t pypi

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Alternative: Install using pip
pip install uv

# Setup and install dependencies
cd prompteng-devs
uv venv .venv --seed
source .venv/bin/activate
uv pip install ipykernel

Configure environment variables:

cp .env-example .env
$EDITOR .env

Rename .env-example to .env and edit the values to match your environment (e.g., API keys or tokens required by your workflow). Ensure .env is present before running notebooks that depend on environment variables.

You can also open the folder directly in VS Code or Cursor and use their built-in notebook support. When prompted for a kernel, select the interpreter from .venv.

prompteng-devs/
├── 01-tutorials/           # Complete learning modules
│   ├── module-01-foundations/
│   ├── module-02-fundamentals/  
│   ├── module-03-applications/
│   ├── module-04-integration/
│   └── prompt-engineering-for-developers.ipynb  # Complete course
├── 02-exercises/           # Hands-on practice
│   ├── hands-on/          # Exercise notebooks  
│   └── solutions/         # Reference solutions
├── 03-examples/           # Real-world patterns
│   ├── code-quality/
│   ├── debugging/
│   ├── api-integration/
│   └── custom-commands/
└── GitHub-Copilot-2-API/  # GitHub Copilot proxy setup

• Kernel: Select the .venv Python interpreter as the notebook kernel
• Execution: Run cells top-to-bottom initially, then iterate as needed
• Experimentation: Create new cells for testing; preserve original examples
• IDE Integration: VS Code/Cursor built-in notebook support recommended

• Total Duration: ~90 minutes
• Session Options:
  • Single 90-minute session, or
  • Three 30-minute focused sessions, or
  • Self-paced over multiple days

This course is designed for:

• Software Engineers looking to integrate AI assistants into their workflow
• Technical Leads wanting to establish team prompt engineering standards
• DevOps Engineers seeking to automate development workflows with AI
• Engineering Managers planning AI-assisted development adoption

By course completion, you'll have:

• ✅ Working Development Environment with AI assistant integration
• ✅ Prompt Engineering Toolkit with reusable patterns and commands
• ✅ Production-Ready Workflows for code quality, debugging, and API integration

Issues and pull requests welcome! Please ensure:

• Examples are minimal, reproducible, and well-documented
• New patterns include both implementation and usage guidance
• Educational content follows the established progression structure