Hybrid Search Interview Questions

Learned sparse representations, inverted index integration, lexical expansion, and fusion with dense ANN.

BM25 + vector fusion, RRF, weighted scores, rerankers, and same-chunk alignment pitfalls.

Medium RAG interview question on hybrid search: bm25 + dense vectors explained — architecture, trade-offs, eval, and production patterns.

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Vector databases power every RAG system, yet most candidates can't explain ANN algorithms or hybrid search. This fundamental question appears in 80% of AI engineering loops. Master dense vs sparse retrieval and when hybrid search wins.

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Medium RAG interview question on pinecone vs weaviate vs pgvector vs opensearch — architecture, trade-offs, eval, and production patterns.

Hard Microsoft company question — enterprise copilot grounding, Graph permissions, tenant isolation, and compliance.