vector search
FDE interview questions tagged vector search, across every topic.
5 questions · 1 unlocked for you
Concepts behind "vector search"
The curriculum that explains the ideas these questions test.
Foundational
Embeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.🧠 Foundations of LLMs & GenAI
Foundational
Retrieval-Augmented Generation (RAG)RAG grounds a language model in your own data by retrieving relevant passages at query time and putting them in the prompt, so the model answers from real sources instead of memory. It is the default pattern for almost every enterprise FDE deployment, which is why nearly every loop tests it.🤖 Retrieval & Agents
Foundational
Vector DatabasesA vector database stores embeddings alongside metadata and answers nearest-neighbor queries fast using approximate indexes. The real interview question is not how they work but when you actually need one instead of a library or plain Postgres with pgvector.🤖 Retrieval & Agents
Core
Hybrid Search (Lexical + Vector)Hybrid search runs a keyword retriever (BM25) and a dense vector retriever side by side, then merges their result lists, because each one misses cases the other catches. Vectors lose exact codes and rare jargon, BM25 loses paraphrase, and combining them with Reciprocal Rank Fusion usually beats either alone.🤖 Retrieval & AgentsSign in
Core
Approximate Nearest Neighbor (ANN)Brute-force vector search is O(N*d) per query and falls apart at millions of vectors, so ANN trades a sliver of recall for orders-of-magnitude speed. The two dominant families are IVF (cluster then probe nearby cells) and HNSW (walk a navigable graph), with product quantization to shrink memory. The non-negotiable habit is measuring recall@k against a brute-force baseline.🤖 Retrieval & AgentsSign in