indexing

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 RAG system is only as current as its index, and stale or duplicated content silently degrades answers. The signal is an incremental pipeline with deletes, updates, and recency signals, not a one-time bulk load. Here is the answer.

Late interaction stores one vector per token, so a corpus that fit in a few GB as single vectors can balloon 100x. The signal is knowing the compression and indexing tricks (centroids, residuals, PLAID) that make multi-vector retrieval shippable.

Query tuning is a core data-engineering skill, and the signal is reading the execution plan before changing anything, then applying the right fix. Here is the diagnostic method that separates guessers from engineers.

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