freshness

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.

Re-embedding 10M documents nightly is wasteful when only 0.5% changed. Interviewers want to see you upsert by stable id, handle deletes and tombstones in an ANN index, and compact before fragmentation tanks recall and latency.

The CRM says the balance is $0 and the ledger says $412. An agent left to its own devices will average them into something confident and wrong. Designing the precedence before the model has to guess is the whole answer.

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

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