vector search

One of the most common AI, ML, and GenAI design rounds. Sketching embed-retrieve-generate is easy; the signal lives in chunking, hybrid retrieval, the rerank/latency tradeoff, and how you prove it works. Here is the structure that scores.

Every RAG system rests on a vector index, and the interviewer wants proof you grasp the recall/latency/memory tradeoff, not just 'use a vector DB.' The signal is why exact search will not scale and how HNSW and IVF place different bets.

A bridge connecting classic DSA and modern RAG. Interviewers want to see you build a working inverted index and a correct BM25 score by hand, reason about its complexity, and then know precisely when you would switch to embeddings and an ANN index instead.

A 0.91 cosine score and a useless passage are not a contradiction. The candidates who score here name the encoding mismatch, the topical-but-not-answering chunk, and why an absolute score cutoff was never going to work.

Every RAG design says 'just filter by tenant_id.' Almost nobody can explain why that one line can quietly cut recall in half. The answer is in what the filter does to the ANN graph.

float32 is far more precision than ranking needs. int8 is close to free, binary is 32x smaller, and the difference between a working binary index and a recall disaster is one word: rescoring.

A dedicated vector database is not always required. When embeddings sit beside your relational data, nearest-neighbor in SQL makes filters and joins easy. Here is how, and the scale where it breaks down.

Multimodal search checks whether you grasp shared embedding spaces and the retrieve-then-rank pattern under a real catalog's scale and freshness. What interviewers reward: CLIP-style joint embeddings, hybrid retrieval, and honest relevance evaluation. This lays out the design.

Video search is image search plus a time axis, and the time axis is the whole problem. A strong answer embeds frames, pools them into scene vectors, indexes with ANN at billion scale, and explains how a multimodal query (clip, text, or both) locates the right moment, not just the right video.