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About this interview
A technical interview on RAG, pitched at the easy 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 what RAG is and why grounding LLM responses in external documents reduces hallucination
Describe the basic retrieve-then-read pipeline: chunking, embedding, vector search, context injection
Compare dense (semantic) retrieval versus sparse (keyword) retrieval and when to combine them
Identify the main quality failure modes: poor chunking, retrieval misses, context overflow
Topics covered
RAG motivationRAG pipeline overviewHallucination reductionEmbeddingsVector storesChunkingDense vs sparse retrievalSparse retrievalSimilarity metricsChunking strategiesEmbedding model selectionContext injectionHybrid retrievalResult fusion
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 RAG solve that a plain LLM prompt cannot?
“What is semantic chunking, and what kind of documents does it serve particularly well?
“How does chunk size affect retrieval precision and LLM synthesis quality — and what is the core tension you are navigating?