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hardGleanDatabricksMicrosoft

How do you do incremental indexing for a RAG system with constant document churn, without a nightly full rebuild?

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.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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.

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Next in this trackHow do you verify that an answer's citations actually support its claims (grounding verification)?Next in this trackHow do you measure faithfulness (hallucination rate) in a RAG system, and what makes it hard to score?Next in this trackContext precision versus context recall: which do you optimize, and how do they trade off in RAG?
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