10 System Design Questions Every AI Engineer Gets Wrong

The AI How · Beginner ·🏗️ Systems Design & Architecture ·2w ago

Key Takeaways

Covers system design questions for AI engineers with answers and common pitfalls

Original Description

Most AI engineering candidates nail 3 out of 10 system design questions. The ones who nail all 10 walk out with $300K+ offers. In this video I'm giving you every single answer — plus the traps most candidates fall into. These 10 questions appear at top AI companies (Google DeepMind, Anthropic, OpenAI, Meta, Databricks). Every question includes the wrong answer most candidates give, the correct answer, and the interviewer trap designed to expose you. Master the 10 system design interview questions asked by top AI companies. Learn the exact answers to land a $300k+ offer. Most candidates only pass three of the ten critical questions asked during a system design interview at top-tier AI firms. This guide breaks down the full list of these questions, ranked by frequency and difficulty. It is designed for engineers aiming for senior or staff roles who need to move beyond basic concepts to secure high-paying offers. Each question comes with the specific trap most candidates fall into, along with the correct approach to solve it. By reviewing these, you will understand how to structure your responses to separate yourself from the average applicant. We cover everything from the easier foundational topics to the complex architectural challenges that define a staff engineer interview. Subscribe for weekly tech interview prep breakdowns, and comment below if you want a deep dive on specific system design patterns. 0:00 Intro — the $300K interview gap 0:18 What we're covering 0:31 Q1 — RAG vs Fine-tuning (100K internal documents) 1:17 Q2 — LLM Evaluation (hint: BLEU score is wrong) 1:56 Q3 — System Design: Q&A over document corpus 3:00 Q4 — AI Hallucinations: how to fix confidently wrong answers 3:40 Q5 — ReAct vs Function Calling (they solve different problems) 4:29 Q6 — LLM Too Slow and Expensive: the layered approach 5:16 Q7 — 128K Context Window: lost in the middle problem 6:01 Q8 — Prompt A/B Testing: why manual testing fails in production 6:59 Q9 — Multi-Agent Syste
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Chapters (11)

Intro — the $300K interview gap
0:18 What we're covering
0:31 Q1 — RAG vs Fine-tuning (100K internal documents)
1:17 Q2 — LLM Evaluation (hint: BLEU score is wrong)
1:56 Q3 — System Design: Q&A over document corpus
3:00 Q4 — AI Hallucinations: how to fix confidently wrong answers
3:40 Q5 — ReAct vs Function Calling (they solve different problems)
4:29 Q6 — LLM Too Slow and Expensive: the layered approach
5:16 Q7 — 128K Context Window: lost in the middle problem
6:01 Q8 — Prompt A/B Testing: why manual testing fails in production
6:59 Q9 — Multi-Agent Syste
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