How This Full-Stack Developer Finally Cracked System Design Interviews

Meet Neel

Neel (name changed) is a backend-leaning full-stack developer at a major enterprise software company with four years of experience in Java and JavaScript. He recently started interviewing with FAANGs and other big tech companies like Uber. During this process, he found that the hardest part in the interview process wasn’t the coding rounds, it was system design.

Why System Design Interviews Are Hard to Prepare For

System design interviews test a kind of thinking engineers don’t typically use day-to-day. At work, design happens collaboratively. In interviews, you're expected to drive the entire process solo; structuring ideas, justifying tradeoffs, and adapting under pressure.

“At work, you have time. You talk to people, clarify requirements. In interviews, you're expected to do all of that yourself, in real-time, and explain it clearly. That's not how most engineers usually work.”

Where Traditional System Design Interview Prep Methods Fall Short

Neel started with the basics: he read DDIA, studied GitHub collections like the System Design Primer, and watched hours of YouTube content, including channels like Jordan Has No Life. But when it came time to interview, the knowledge didn’t translate. He lacked hands-on practice under realistic interview conditions.

Peer mock interviews didn’t help either. No one was pointing out flaws or challenging him to improve.

“You spiral into your own wrong doings... how will you know you’re wrong?”

Why Low-Level Design Matters More Than You Think

Most prep content emphasizes high-level design: scalability, global distribution, sharding. But many interviews focus more on low-level design: defining APIs, modeling data, reasoning through use cases.

“I was watching a lot of HLD-focused prep, and I think I over-indexed on scaling and sharding. But in some interviews, they really wanted to see how I’d structure the APIs and think through actual use cases first.”

This mismatch between prep and reality is common. It’s where many candidates get stuck.

If you're preparing for system design interviews, you’ve probably experienced this:

• You’ve studied the right content, but still freeze when asked to think on your feet
• You don’t know what you’re missing
• You’re focused on scale when the interviewer is testing for low level design.

The Breaking Point

In one big tech interview, Neel was asked to design a non-traditional payment system. It wasn’t about scale. It hinged on a domain-specific insight: batching payments to cut fees. That nuance wasn’t obvious in the question asked. By the time he realized it, it was too late.

What he learned:

• Don’t assume every question  is about scale
• Many problems are tied to company-specific realities
• Early feedback can change your answer’s trajectory

Then He Tried Peppermint AI

Neel found Peppermint AI through a LeetCode thread. It immediately changed how he approached system design prep.

Instead of diving into architecture diagrams, Peppermint AI slowed him down: starting with API definitions, data modeling, and functional requirements. In one early session, he was asked to design a basic notifications service. Rather than jumping to queues or DBs, the focus stayed on inputs, confirmations, and edge cases.

“It really opens the mind to how to come to a question… the right way to first functionally solve something and then solve for scale.”

Why Peppermint AI Is Different

Peppermint AI mimics a real interview structure. You walk through multiple stages: from scoping to APIs to tradeoffs. The system pushes back when your thinking is weak. It forces you to explain decisions and adjust in real time.

The AI interviewer wasn’t overly agreeable like ChatGPT: it questioned him, redirected him, and helped him reason better through feedback.

This kind of friction is what makes it work. It’s not about memorizing patterns, it's about learning to think clearly from first principles under time pressure.

What’s Next for Neel

Neel is still in the middle of interviewing, but his skills have significantly improved.

• He now uses Peppermint AI to simulate interviews and sharpen his thinking
• He knows his weak spots and how to improve
• He walks into interviews more confident and prepared

Would He Recommend It?

Absolutely.

“I’ve already shared it with friends. I tell other engineers to try it for one session, because that’s all it took for me to realize it works.”

How Peppermint AI Can Help  

If you’ve been stuck in passive prep (watching videos, reading books, and scrolling GitHub) you’re not alone. Peppermint AI helped Neel shift from consuming to practicing. That’s where real progress happens. If you're tired of second-guessing yourself in interviews, Peppermint AI gives you a place to build confidence through actual practice.