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.
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 World-Class AI Engineer Cohort
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
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"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
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
Senior AI Roles Demand Production LLM Knowledge. Prepare Now.
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.
I've signed up for cohorts before and dropped out. How is this different?
It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.
I'm not pivoting careers. I want to build a product. Does this still work?
Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.
Do I need prior AI experience?
You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
What does it cost?
It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.
Can I do this while working full-time?
Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.