LLM Application Design Interview:
Architecture Patterns That Impress
Beyond RAG, interviewers test your ability to design complete LLM applications.
Learn the patterns, trade-offs, and production concerns they evaluate.
AI Native Engineer Community Access
LLM Design Questions
Go Beyond RAG
You've built prototypes but struggle to discuss production architecture decisions.
You're not sure how to address safety, guardrails, and error handling in design discussions.
Interviewers ask about cost optimization, but you've only used APIs without tracking spend.
Design LLM Applications Like a Senior
The AI Career Accelerator
LLM application design interviews test your understanding of the full stack: prompt engineering, API patterns, error handling, safety, and cost management.
Prompt Architecture
System prompts, dynamic prompts, few-shot patterns, and prompt versioning
API Integration
Streaming, retries, fallbacks, and multi-model routing
Safety & Quality
Input validation, output filtering, guardrails, and evaluation
Production Concerns
Caching, cost management, monitoring, and scaling strategies
Meet Your Mentor
When I started in tech, I was based in the Netherlands with no connections and only thousands of video game hours under my belt. Not exactly the ideal starting point.
My first tech job was software tester. One of the most junior roles you can start with. I was just happy someone took a chance on me.
I kept learning. Kept pivoting. But what actually accelerated my career wasn't more certifications or more code. It was learning to solve problems that matter and proving beyond a doubt that what I built solved real problems. That's the skill that stays future-proof, even with AI.
I've since worked remotely for international software companies throughout my career. Proof that the high-paid remote path is possible for anyone with the right skills and motivation. In the end, I went from a $500/month internship to 6 figures as a Senior AI Engineer at GitHub.
Now I teach over 22,000 engineers on YouTube. Becoming an AI-Native Engineer is a system I lived through and offer to you today.
Real Results
Vittor
AI Engineer
Landed his first AI Engineering role in 3 months
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
Personalized Roadmap & Career Strategy
A custom plan tailored to your background, goals, and timeline. No generic advice.
Weekly 1:1 Coaching Calls
Direct access to Zen for guidance, project feedback, and answers to your questions.
Portfolio-Ready AI Projects
Build production-grade AI applications to showcase to employers. Work that gets you hired.
Interview Prep & Mock Interviews
Practice technical and behavioral interviews. Learn what hiring managers look for.
Resume & LinkedIn Optimization
Transform your online presence to attract recruiters. Stand out from other applicants.
Community Career Support
Join the AI Native Engineer community. Not seeing results yet? You stay and keep going. We're with you through the ups and downs.
Senior AI Roles Demand Production LLM Knowledge. Prepare Now.
Every month you delay can cost you thousands in lost earning potential. While you're watching tutorials, others are landing $120K+ AI Engineering roles.
I can only work with a limited number of 1:1 clients at a time to ensure you get the personalized attention you deserve.
Frequently Asked Questions
What LLM application design questions do interviewers ask?
Common questions: Design an AI writing assistant, Build an AI customer support agent, Design a code review system with LLMs, Create a content moderation pipeline, Design an AI-powered search with natural language queries. Each tests your ability to combine LLM capabilities with traditional software architecture.
How should I discuss prompt engineering in design interviews?
Cover: (1) System prompts for consistent behavior and constraints, (2) Dynamic prompts with context injection, (3) Few-shot examples for output formatting, (4) Prompt versioning for iteration and rollback. Discuss trade-offs: longer prompts = more control but higher cost/latency. Show you think about prompts as code that needs testing and versioning.
What error handling patterns should I know for LLM applications?
Key patterns: (1) Retry with exponential backoff for rate limits, (2) Fallback to smaller/faster models when primary fails, (3) Graceful degradation—return cached or partial results, (4) Circuit breakers to prevent cascade failures, (5) Input validation to reject malformed requests early. Discuss monitoring: track error rates, latency p99, and cost per request.
How do I discuss LLM cost optimization in interviews?
Strategies to mention: (1) Prompt caching for repeated contexts, (2) Response caching for frequent queries, (3) Model routing—use smaller models for simple tasks, (4) Batch processing when real-time isn't required, (5) Prompt optimization to reduce token count. Quantify: GPT-4 costs 10-30x more than GPT-3.5—discuss when each is appropriate.
When should I recommend multi-model architectures?
Use multiple models when: (1) Different tasks have different quality/cost requirements, (2) You need fallbacks for reliability, (3) Specialized models outperform general ones (e.g., code vs. text), (4) You want to reduce vendor lock-in. Discuss routing logic: classifier-based, rule-based, or cascading approaches.
Do I need production LLM experience for these interviews?
Helpful but not required. Study: (1) Production patterns from engineering blogs, (2) Build small projects implementing these patterns, (3) Understand cost/latency trade-offs from documentation. Interviewers evaluate your reasoning about production concerns, not just whether you've deployed at scale.
How much time do I need to commit?
Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.
What if I don't land interviews in 90 days?
You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.
How is this different from online courses?
Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.
What's the investment for 1:1 coaching?
Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.
Can I do this while working full-time?
Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.
Ready to Land Your AI Role?
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30-minute strategy call • Limited spots available