How do you handle RAG for real-time or frequently updating data sources?

RAG over real-time data requires incremental indexing rather than periodic full rebuilds. Implement change data capture on source systems to detect new, modified, and deleted documents. New documents are immediately chunked, embedded, and added to the vector index. Modified documents trigger re-chunking and re-embedding of affected sections with old embeddings replaced. Deleted documents have their embeddings removed from the index. For streaming data sources like news feeds or market data, a processing pipeline continuously ingests, chunks, and embeds new content. Set a maximum document age for time-sensitive sources and exclude stale content from retrieval. For databases, use triggers or CDC tools like Debezium to detect changes. Implement near-real-time sync with a target delay under 5 minutes for most enterprise use cases. Monitor index freshness metrics and alert when sync falls behind. Use timestamp-based filtering to ensure the most recent information is prioritized.