A structured framework for answering Graph RAG interview questions — knowledge graph construction, entity extraction, community detection, and Microsoft's GraphRAG approach.
A framework for answering hybrid search interview questions in RAG systems — BM25 + dense retrieval, reciprocal rank fusion, when sparse beats dense, and weighted scoring in practice.
A structured framework for answering RAG metadata filtering interview questions — pre-filter vs post-filter tradeoffs, schema design, access control, and temporal filtering.
A structured framework for answering multimodal RAG interview questions, covering image+text retrieval, CLIP embeddings, table extraction, and diagram understanding.
A structured framework for answering RAG query rewriting questions in AI engineering interviews — covering HyDE, step-back prompting, multi-query decomposition, and LLM-based query expansion.
How to answer RAG reranking interview questions — cross-encoder rerankers, ColBERT, Cohere Rerank, the two-stage retrieval pipeline, and how to manage the latency budget.