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Core
Vector Search and ANN Indexes
Vector search finds the embeddings nearest to a query vector. Exact nearest-neighbor is O(n) per query and does not scale, so production uses Approximate Nearest Neighbor (ANN) indexes (HNSW, IVF, product quantization) that trade a little recall for massive speedups. The real-world challenges are the recall-vs-latency-vs-memory trade-off, metadata filtering, and handling updates. Applied-AI interviews probe it because it is the engine under RAG and semantic search, and its tuning directly sets retrieval quality and cost.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionDesign a vector database / embedding retrieval service.→System Design for AI in ProductionDesign a two-tower retrieval system for recommendation/candidate generation.→RAG & Agent System DesignWhat is HyDE (Hypothetical Document Embeddings), and why does it improve retrieval?→RAG & Agent System DesignWhat is semantic chunking, and how does it compare to fixed-size chunking?→System Design for AI in ProductionDesign a RAG-as-a-service platform that lets teams build retrieval-augmented apps over their own data.→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→
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