AI Product Engineering Interview Guide

Curated questions covering the complete AI product engineering stack. Perfect for interview preparation, hiring decisions, and skill development.

🎯 The Pitch

The AI landscape is evolving rapidly, but interview preparation hasn't kept up. Most resources focus on theory or research, leaving a gap for practical AI product engineering skills. This guide bridges that gap.

Why This Matters

• For Candidates: Master the skills that actually matter in AI product roles
• For Hiring Managers: Assess candidates on real-world AI engineering capabilities
• For Teams: Identify skill gaps and create learning paths
• For the Industry: Standardize expectations for AI product engineering roles

What Makes This Different

• 500+ Curated Questions across 13 essential domains
• Progressive Difficulty: Entry level → Staff/Principal engineering
• Product-Focused: Real scenarios, not academic theory
• Battle-Tested: Questions from experienced AI engineers at top companies
• Always Current: Regular updates as the field evolves

🚀 Live Site

👉 aiproductengineerinterview.com

📋 Question Categories

1. Prompt Engineering - Communicating effectively with LLMs
2. Context Engineering - Structuring and optimizing context
3. RAG (Retrieval-Augmented Generation) - Knowledge-grounded AI systems
4. Model/API Selection - Choosing the right tools for your product
5. AI Agent Engineering - Building autonomous, tool-using systems
6. Agentic Techniques - Advanced patterns for complex workflows
7. Model Optimization - Performance tuning and cost optimization
8. AI System Evaluation - Measuring and benchmarking performance
9. Responsible AI - Ethics, safety, and governance
10. AI UX Design - Designing user experiences for AI features
11. Feedback Loops - Learning from user and model feedback
12. Rapid Prototyping - Fast iteration with AI systems
13. Multimodal AI - Combining text, vision, and audio

🛠 Local Development Setup

Prerequisites

• Node.js 18+ and npm
• Git for version control

Quick Start

# Clone the repository
git clone https://github.com/SilasReinagel/AiProductEngineerQuestions.git
cd AiProductEngineerQuestions

# Install dependencies
cd webapp && npm install && cd ..

# Start development server
npm run dev

The app will be available at http://localhost:5173

Available Scripts

# Development
npm run dev              # Start dev server with hot reload

# Building
npm run build            # Build for production
npm run preview          # Preview production build locally

# Deployment
npm run deploy           # Deploy to Netlify (production)
npm run deploy:preview   # Deploy preview to Netlify

# Marketing
npm run utm:generate     # Generate UTM tracking links

Project Structure

AiProductEngineerQuestions/
├── design/              # Design docs and specifications
├── marketing/           # UTM tracking and marketing assets
│   ├── scripts/         # UTM link generator
│   └── dist/           # Generated marketing links
├── pmt/                # Project management and tasks
├── webapp/             # Main React application
│   ├── public/         # Static assets and question data
│   │   └── questions/  # JSON question files
│   ├── src/
│   │   ├── components/ # React components
│   │   ├── hooks/      # Custom React hooks
│   │   ├── data/       # Compiled question data
│   │   └── config/     # Configuration files
│   └── scripts/        # Build and compilation scripts
└── README.md           # This file

📝 Contributing

We welcome contributions! Here's how to get involved:

Types of Contributions

• Question Improvements: Better wording, new examples, difficulty adjustments
• New Questions: Fill gaps in existing categories
• Category Expansion: Propose new domains as AI evolves
• Bug Fixes: UI issues, mobile improvements, accessibility
• Feature Requests: New functionality to improve the experience

How to Contribute

1. Fork the Repository

   git fork https://github.com/SilasReinagel/AiProductEngineerQuestions.git

2. Create a Feature Branch

   git checkout -b feature/your-feature-name

3. Make Your Changes

   • Questions: Edit JSON files in webapp/public/questions/
   • Code: Follow existing patterns and add JSDoc comments
   • UI: Test on mobile and desktop, maintain accessibility

4. Test Locally

   npm run dev
   # Verify your changes work correctly

5. Submit a Pull Request

   • Clear title and description
   • Link any related issues
   • Include screenshots for UI changes

Question Contribution Guidelines

When adding or modifying questions:

• Practical Focus: Real scenarios over theoretical concepts
• Clear Wording: Unambiguous, professional language
• Appropriate Difficulty: Match the target skill level
• Diverse Perspectives: Consider different company sizes and contexts
• Current Relevance: Reflect modern AI engineering practices

Code Contribution Guidelines

• Plain JavaScript + JSDoc: No TypeScript, use // @ts-check
• ES6 Modules: Use import/export, not CommonJS
• Tailwind CSS: For styling, follow existing patterns
• Accessibility: Maintain WCAG 2.1 AA compliance
• Mobile-First: Ensure responsive design

🏗 Technical Architecture

Frontend Stack

• React 18 with hooks and functional components
• Vite for fast development and building
• TailwindCSS for styling with custom design system
• React Router for client-side routing
• Static Data Compilation for fast loading

Key Features

• Static Question Compilation: Questions compiled at build time for performance
• Responsive Design: Mobile-first with excellent tablet/desktop experience
• Progressive Enhancement: Works without JavaScript for core functionality
• Analytics Integration: Plausible Analytics with custom event tracking
• SEO Optimized: Proper meta tags, sitemap, and structured data

Performance Optimizations

• Static Data: No runtime API calls for questions
• Code Splitting: Lazy loading for optimal bundle sizes
• Image Optimization: Proper formats and sizes
• Caching Strategy: Aggressive caching for static assets

📊 Analytics & Tracking

We use privacy-focused analytics to understand usage patterns:

• Plausible Analytics: Privacy-friendly, no cookies
• UTM Tracking: Campaign attribution for marketing
• Custom Events: Category selections, search usage, shares

🚀 Deployment

The site is deployed on Netlify with:

• Automatic Deploys: From main branch
• Preview Deploys: For pull requests
• Form Handling: For any future contact forms
• Edge Functions: For any server-side logic

Getting the Netlify Badge

After deploying to Netlify:

1. Go to your site's dashboard in Netlify
2. Navigate to Site Settings → General → Status badges
3. Copy the badge markdown and replace the commented badge at the top of this README

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Acknowledgments

• Contributors: All the engineers who've shared their interview experiences
• Community: AI engineering community for feedback and validation
• Open Source: Built with amazing open source tools

📬 Contact

• Website: aiproductengineerinterview.com
• GitHub: Issues and Discussions
• Author: Silas Reinagel

---

⭐ Star this repo if it helps you land your next AI engineering role or hire great AI talent!