Prompt Engineering for Developers

Master prompting techniques for software development with structured tutorials, hands-on exercises, and real-world examples.

🚀 Get Started

1. Clone the repository:

git clone git@github.com:splunk/prompteng-devs.git
cd prompteng-devs

2. Begin learning:

• Start Module 1: Foundations ← Read README.md, then open the .ipynb notebook
• View All Modules ← Browse the complete course
• Implementation Examples ← Production patterns

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🎯 Recommended Learning Workflow

📚 For Each Module:

Step 1: 📖 Read the Module

• Open the module's README.md file to understand learning objectives and prerequisites

Step 2: 🚀 Launch the Notebook

• Open the .ipynb notebook file to begin the interactive tutorial

Step 3: 💻 Complete All Cells

• Run through each cell sequentially from top to bottom

Step 4: 🏃‍♀️ Practice Exercises

• Complete the hands-on exercises to reinforce learning

Step 5: 📊 Self-Assess

• Use the Skills Checklist in the notebook to track your progress

Step 6: ➡️ Next Module

• Move to the next module and repeat the process

📈 Track Progress: Use the Skills Checklist in each notebook to mark skills as you master them 🚀 Apply Skills: Use real-world examples after completing all modules

💡 Tip: Each module directory contains a README.md file explaining what you'll learn and how to get started.

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⚡ Quick Setup

Prerequisites: Python 3.8+, IDE with notebook support, API access (GitHub Copilot/CircuIT/OpenAI)

# 1. Clone the repository
git clone git@github.com:splunk/prompteng-devs.git
cd prompteng-devs

# 2. Install dependencies
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv .venv --seed
source .venv/bin/activate
uv pip install ipykernel

# 3. Configure environment
cp .env-example .env
# Edit .env with your API keys

Splunk users: Run okta-artifactory-login -t pypi before installing dependencies.

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📓 About Jupyter Notebooks

🆕 First time using Jupyter notebooks? Read this section before starting the modules.

All course modules use Jupyter notebooks (.ipynb files) - interactive documents that let you run code directly in your IDE.

⚠️ Important Requirements

You must clone this repository and run notebooks locally. They cannot be executed directly from GitHub.

💡 How Notebooks Work

• Code cells contain Python code that runs on your local machine
• Click the ▶️ button (or press Shift + Enter) to execute a cell
• Output appears below each cell after you run it
• To edit cells: Double-click to edit, make changes (like uncommenting code), then press Shift + Enter to run
• Installation commands run locally and install packages to your Python environment
• You don't copy/paste - just click the run button in each cell
• Long outputs are truncated: If you see "Output is truncated. View as a scrollable element" - click that link to see the full response

🔒 Where Code Executes

All code runs on your local machine. When you:

• Install packages → They're installed to your Python environment
• Connect to AI services → Your computer sends requests over the internet to those services
• Run API calls → They execute from your machine using your credentials

🚀 Getting Started with Notebooks

1. Open the .ipynb file in your IDE (VS Code or Cursor recommended)
2. Select the Python kernel: Choose your .venv interpreter when prompted
3. Run cells sequentially from top to bottom
4. Complete exercises as you go through the modules
5. Experiment: Add new cells to try your own code

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📊 Tracking Your Progress

Each module includes a Skills Checklist to help you track your mastery of prompt engineering techniques.

How It Works

Each module notebook has two sections for tracking progress:

1️⃣ Progress Overview (Visual Status Only - Not Interactive)

• Shows automatic status: Tutorial completion and overall progress
• These checkmarks (✅/⬜) are visual indicators only - you cannot click them
• Automatically shows ✅ for "Tutorial Completed" after you finish all cells
• The ⬜ for "Skills Mastery" reminds you to use the Skills Checklist below

2️⃣ Check Off Your Skills (Interactive Checkboxes - This is Where You Track!)

• Contains clickable checkboxes for each individual skill
• This is where you actively track your mastery as you learn
• Check off each skill as you achieve it (see criteria below)
• Your progress percentage updates automatically based on checked skills

When to Check Off a Skill

✅ You can confidently apply the technique without referring back to examples
✅ You understand why and when to use the technique
✅ You can explain the technique to a colleague
✅ You've successfully used it in your own coding tasks

💡 Important: The interactive checkboxes are in the "Check Off Your Skills" section. Don't worry if you can't click the status indicators in "Progress Overview" - those are just visual guides!

💡 Tip: Don't rush to check off skills. The goal is genuine mastery, not completion speed. Come back and practice skills until you feel confident.

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📚 Learning Path

1. Interactive Course - Learn the fundamentals

• Module 1: Foundations - Interactive notebook (.ipynb) with environment setup & prompt anatomy (20 min)
• Module 2: Core Techniques - Interactive notebook (.ipynb) with role prompting, structured inputs, few-shot examples, chain-of-thought reasoning, reference citations, prompt chaining, and evaluation techniques (90-120 min)
• Module 3: Applications - Interactive notebook (.ipynb) with code quality, testing, debugging (30 min)
• Module 4: Integration - Interactive notebook (.ipynb) with custom commands & AI assistants (10 min)

2. Practice - Reinforce learning

• Hands-on Exercises - Integrated into each module to reinforce concepts
• Self-Assessment - Use the Skills Checklist in each module to track your progress

3. Apply - Real-world patterns

• Code Quality - Refactoring & modernization
• Debugging - Incident investigation & resolution
• API Integration - Client generation & error handling
• Custom Commands - Reusable templates

🎯 What You'll Build

• ✅ 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

Total Time: ~90 minutes (can be split into 3×30min sessions)

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📁 Project Structure

prompteng-devs/
├── 01-course/                    # Learning modules
├── 02-implementation-examples/   # Real-world patterns
└── GitHub-Copilot-2-API/         # Copilot setup

New to notebooks? See About Jupyter Notebooks section above.

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🤝 Contributing

Issues and pull requests welcome! Ensure examples are minimal, reproducible, and well-documented.