Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Retrieval-Augmented Generation, pitched at the hard 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 query rewriting, HyDE, and multi-query expansion and why they improve recall
Describe re-ranking with cross-encoders and how it differs from bi-encoder retrieval
Explain RAG-Fusion and reciprocal rank fusion for combining multiple retrieval results
Describe how to evaluate RAG pipelines using context precision, context recall, faithfulness, and answer relevance
Topics covered
Query Quality & RecallQuery RewritingHyDEMulti-Query ExpansionQuery Transformation StrategiesRe-ranking MotivationBi-Encoder vs Cross-EncoderCross-Encoder ScalabilityRetrieval Pipeline TuningRe-ranking AlternativesRAG-FusionReciprocal Rank FusionRAG-Fusion PipelineRAG-Fusion Trade-offs
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In a naive RAG pipeline, users often get back irrelevant documents even when the answer is clearly in the corpus. What are the most common root causes of poor retrieval recall, and how do query-side techniques address them?
“What are the trade-offs of RAG-Fusion compared to a single-query retrieval approach? When does the additional complexity not pay off?
“What is contextual compression in retrieval, and how does it help with the problem of noisy or overly long retrieved chunks being passed to the LLM?