indexing

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 RAG system is only as current as its index, and stale or duplicated content quietly degrades answers. The signal is an incremental pipeline with deletes, updates, and recency signals, rather than a one-time bulk load. Here is the answer.

Late interaction keeps one vector per token, so a corpus that fit in a few GB as single vectors can swell 100x. Interviewers want to see you know the compression and indexing tricks (centroids, residuals, PLAID) that make multi-vector retrieval shippable.

Query tuning is a core data-engineering skill, and what interviewers watch for is reading the execution plan before you change anything, then applying the right fix. Here is the diagnostic method that tells guessers apart from engineers.

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.