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.
How to answer vector index questions in RAG interviews — HNSW vs IVF vs flat, Pinecone vs Weaviate vs Qdrant, the recall-latency tradeoff, and what changes at billion-scale.