How to detect stale search data in long-running RAG systems
A RAG system can slowly fall out of sync with its original data after running for a long time. Old text chunks may remain after documents are updated, and may survive after their source database rows are deleted. Re-ingesting data can also create near-duplicate copies that compete with each other during .
Deleting an item from a database therefore does not guarantee that the AI can no longer find it. The main operational questions are how to detect and measure this drift, whether to run scheduled jobs, and how to verify deletions before users encounter outdated .
Key points
- Updated documents can leave old text chunks in the .
- Deleting source rows may not remove their .
- Re- can create near-duplicates that weaken .
- Deletion should be verified in both the database and actual search results.
- Scheduled can catch stale data before a user reports it.