ann

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.

At a billion vectors the index choice is a memory budget question before it's a recall question. Flat search is off the table, HNSW may not fit, and PQ swaps recall for RAM. Here is the call a staff engineer makes on the whiteboard.

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.

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.

How large recommenders and search draw candidates from millions of items in milliseconds. The signal is why the user and item towers stay separate, how that enables precomputed embeddings plus an ANN index, and where ranking takes over.

The service under every RAG stack and semantic search box. The signal is your ANN index choice and how you deal with the three things that quietly break it: metadata filtering, live updates, and scale.

Image search is embedding retrieval at billion-image scale. A strong answer shows when a vision-only encoder is enough and when you need a CLIP-style shared space, plus how the ANN index actually serves the query. Here is 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.