freshness

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.

Re-embedding 10M documents nightly is wasteful when only 0.5% changed. The signal is upserting by stable id, handling deletes and tombstones in an ANN index, and compacting before fragmentation tanks recall and latency.

A news feed is not a generic recommender. The clock is a first-class signal: a story that mattered this morning is noise by tonight. The interviewer wants recency decay, an engagement-versus-quality value model, and an answer for filter bubbles, not just retrieval plus ranking.

A fraud model fed a feature an hour behind is half-blind, but recomputing everything in real time burns money you don't need to spend. Freshness is a per-feature decision on a real cost curve. Here is how to manage it.