Preview — Pro guide
You are seeing a portion of this guide. Sign in and upgrade to unlock the full article, quizzes, and interview answers.
Sections
Related Guides
Embeddings — From word2vec to Instruction-Tuned Vectors & Production RAG
GenAI & Agents
RAG Architecture: From Basics to Production
GenAI & Agents
LLM Serving at Scale: vLLM, KV Cache, Batching, and LLMOps
GenAI & Agents
Embeddings & Vector Databases: ANN Search at Scale
ML System Design
LLM Fundamentals — Transformers, Attention & Architecture
GenAI & Agents
Vector Search for GenAI: HNSW, IVF-PQ, FAISS, and ScaNN in Production
Standalone deep dive on vector search systems for GenAI workloads. Learn how HNSW, IVF, IVF-PQ, and ScaNN differ on recall-latency-cost, how to tune parameters like efSearch and nprobe, and how to choose the right index for million-to-billion scale retrieval.
Why Vector Search Is a Separate Interview Topic
Most candidates collapse vector search into "RAG plumbing." Staff-level interviewers do not. Retrieval quality is usually bounded by index behavior, not prompt quality. If your retriever misses the right chunks, the generator cannot recover.
The non-obvious point: index choice is a business decision. HNSW can deliver high recall and low latency, but memory grows quickly. IVF-PQ cuts memory dramatically, but loses fidelity and needs careful tuning plus reranking. In production, this decision directly controls cloud bill and hallucination rate.