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.

The article argues that interview bar is now system design, not RAG basics, and walks through seven recurring questions: designing an end-to-end RAG system and evaluating it, differentiating RAG from agentic/Corrective RAG and when to use it, designing an agent that performs real actions with a hard safety rule enforced outside the model, clarifying what logic belongs in the orchestrator versus the LLM, debugging hallucinations or loops live by separating retrieval vs generation failures, controlling cost/latency at scale via batching, caching, routing, and context trimming (plus avoiding unnecessary multi-agent overhead), and evaluating RAG/agents both pre- and post-shipping by splitting retrieval and generation metrics and defining agent success and efficiency. It concludes with the frameworks worth naming, common mistakes across all questions (staying abstract, prompt-fixing architectural issues, over-recommending the most complex setup, and skipping failure modes), plus practical preparation guidance and how question depth varies by seniority.

Read the full blog for free on Medium.

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Published via Towards AI