7 RAG & Agent System Design Questions You Will Face in Every AI Engineer Interview (With Answers)

A friend failed a senior AI engineer interview at an undisclosed company after being asked what happens when a retriever pulls a contradictory document, highlighting that RAG fundamentals are now just the entry fee. The article presents seven system design questions commonly asked at Anthropic, Scale AI, Sierra, xAI, and Glean, with model answers focused on architecture.

Member-only story 7 RAG & Agent System Design Questions You Will Face in Every AI Engineer Interview With Answers I watched a friend walk into a senior AI engineer loop last month with a portfolio full of solid RAG projects and a Medium-article-level understanding of agents. He drew a clean retrieval pipeline on the whiteboard, explained cosine similarity without stumbling, and felt good about it. Then the interviewer asked what happens when the retriever pulls back a document that contradicts what the user actually meant. He said he’d tune the prompt. He didn’t get the offer. That question wasn’t a trick. It’s the new baseline. Reports from candidates interviewing at Anthropic, Scale AI, Sierra, xAI, and Glean through 2026 show the same pattern: RAG fundamentals are no longer the bar, they’re the entry fee. The real interview starts one level up, at system design questions that assume you already know what a vector database is and want to know what you do when the system you built breaks in a way a tutorial never covers. This piece walks through seven questions that show up across those loops, again and again, with the kind of model answer that actually survives a follow-up question. Not definitions. Architecture.