vector search

The modal Applied AI design round. Anyone can draw embed-retrieve-generate. The signal is 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 to know you understand the recall/latency/memory tradeoff, not just 'use a vector DB.' The signal is why exact search does not scale and how HNSW and IVF make different bets.

A bridge between classic DSA and modern RAG. The signal is building a working inverted index and a correct BM25 score by hand, reasoning about its complexity, and then knowing exactly when you would reach for embeddings and an ANN index instead.

You don't always need a dedicated vector database. When embeddings live next to your relational data, nearest-neighbor in SQL makes filters and joins trivial. Here is how, and the scale where it breaks.

Multimodal search tests whether you understand shared embedding spaces and the retrieve-then-rank pattern under a real catalog's scale and freshness. The signal is CLIP-style joint embeddings plus hybrid retrieval and honest relevance evaluation. Here is the design.

Video search is image search with 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) finds the right moment, not just the right video.