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

Author(s): allglenn Originally published on Towards AI. 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. AI Engineer interviewAfter the intro, the article explains why RAG-focused questions no longer define the bar: system design now probes whether you can make solid, defensible choices under real constraints and failure modes. It then walks through seven recurring interview prompts—designing end-to-end RAG with evaluation, distinguishing RAG vs agentic RAG and routing by complexity, building an action-taking agent with safety rules that can’t be bypassed, clarifying what belongs in the orchestrator versus the LLM, debugging hallucinations or infinite loops live by separating retrieval vs generation failures, controlling cost and latency as usage scales (batching, caching, routing, trimming context, and avoiding unnecessary multi-agent overhead), and evaluating RAG/agents both pre- and post-shipping by splitting retrieval and generation metrics plus agent task success, tool correctness, and step efficiency. Throughout, it emphasizes naming concrete tools and metrics, addressing failure cases, using observable stage-level traces, and preparing with real projects and evaluation baselines rather than relying on definitions or prompt tweaks. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI