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About this interview
A technical interview on Retrieval-Augmented Generation, 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
Explain the three phases of a RAG pipeline: ingestion, retrieval, and synthesis
Describe chunking strategies (fixed-size, recursive, semantic) and the trade-offs for different document types
Explain dense vs sparse retrieval and when hybrid retrieval is preferred
Describe embedding model selection criteria and how task-specific models affect retrieval quality
Topics covered
RAG motivationRAG pipeline overviewIngestion phaseRetrieval phaseSynthesis phaseChunking motivationFixed-size chunkingRecursive chunkingSemantic chunkingChunking strategy selectionChunk overlapChunk size trade-offsDense retrievalSparse retrieval
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what problem does Retrieval-Augmented Generation solve, and why would you reach for it instead of just relying on the base language model?
“In what situations does sparse retrieval outperform dense retrieval, even with a state-of-the-art embedding model?
“Walk me through how you would design a RAG pipeline for a medical knowledge base where precision — only answering from verified sources — is critical.