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About this interview
A technical interview on RAG, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Contrast static RAG pipelines with agentic RAG where the model owns its own retrieval loop
Explain the maker-checker pattern: iterative LLM calls, query rewriting, and tool-based retrieval
Describe how an agentic RAG system handles failure modes such as low-relevance results or malformed queries
Discuss governance tradeoffs: when to let the agent iterate freely versus when to enforce guardrails
Topics covered
Agentic RAG vs Static RAGAgentic RAG LoopMaker-Checker PatternFailure Mode HandlingGovernance and TransparencyUse Cases and Trade-offsTool Integration
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“How would you describe the core difference between a traditional RAG pipeline and an agentic RAG system to someone who already knows basic RAG?
“How does an agentic RAG system decide that it has gathered enough information and should stop iterating? What criteria would you design into that stopping condition?
“How does an agentic RAG system decide which retrieval method to use on a given iteration — vector search, keyword search, SQL query, or external API? What factors drive that routing decision?