Interview Prep

System design questions, behavioral prompts, and the mental models behind them. Work through these after completing Levels 3–5.

System Design

Each prompt includes hints — try to answer without them first, then use the hints to pressure-test gaps.

STAR format for each. Have at least one concrete story ready per question.

Tell me about a system you built that failed in production. How did you diagnose and fix it?

STAR format. Emphasise what you learned and the observability tooling you added afterward.

Describe a time you had to make a meaningful trade-off between model quality and latency or cost.

Concrete numbers help (e.g., "dropping from GPT-4 to Haiku cut cost 10x with 5% quality drop on our eval set").

How do you decide when an LLM feature is "good enough" to ship? Walk me through your evaluation process.

Shows eval maturity. Mention golden datasets, LLM-as-judge, user study, and rollout strategy.

How would you explain hallucinations — and the limits of your mitigation strategies — to a non-technical stakeholder?

Tests communication. Frame around user impact and the system guardrails, not just model internals.

How do you stay current with the LLM landscape given the pace of change? What did you read or build last week?

Have a genuine, specific answer. Arxiv Sanity, Hugging Face blog, Chip Huyen newsletter, and actual side projects are all valid.

Describe a project where you owned both the ML component and the backend/infra. What did you learn?

LLM engineering roles blur the ML/backend boundary. Show you are comfortable in both.

Tell me about a time you pushed back on using an LLM when a simpler solution was better.

Shows engineering judgement. LLMs are not always the right tool.

How do you work with product/design on AI features where user expectations are hard to set?

Focus on setting clear eval criteria upfront, iterating with prototypes, and managing "AI magic" expectations.