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AI engineer interview playbooks
Practical answer frameworks, production tradeoffs, and follow-up angles for real AI engineering interviews. Read them like field notes, not marketing pages.
How do you evaluate a RAG system before shipping it to production?
Open by separating retrieval evaluation from generation evaluation. They fail for different reasons, so I do not score them as one fuzzy quality number. Then I commit to one concrete release path: a frozen golden set…
What is an AI agent, and how does it differ from a simple LLM call?
The trap here is agent hype. A weak answer treats an agent like an LLM plus tools. The interviewer is usually probing whether you understand the operational difference between one-shot generation and a loop that can…
What is evaluation-driven development for AI applications?
The interviewer is usually testing whether you build AI features with the same seriousness you would bring to shipping any other production system. A weak answer says "we test prompts manually." A strong answer…
What are foundation models, and how have they changed AI engineering?
The interviewer is not asking for a textbook definition. They want to know whether you understand why the engineering stack changed once large pre-trained models became usable product primitives instead of research…
Explain the AI product lifecycle from ideation to production.
This question sounds broad because the interviewer wants to see whether you can impose a release discipline on a probabilistic system. A weak answer gives the normal software lifecycle. A strong answer explains where…
What is Retrieval-Augmented Generation (RAG), and why is it important?
The trap here is giving a buzzword answer about embeddings and vector databases. The interviewer is really testing whether you understand what RAG buys in production: freshness, private knowledge access, attribution,…
Design an AI-powered customer support chatbot.
The interviewer is not looking for a vendor shopping list. They want to see whether you understand that support bots sit on top of trust, policy, and customer operations. The strong answer separates informational help…