Originally published on AI Tech Connect.
There is a whole literature on making retrieval accurate and almost none on making it current. Chunking, hybrid search, index parameters, rerankers and evaluation metrics are well covered — our guides on chunking and embedding strategies, hybrid retrieval, vector index tuning, reranking and RAGAS evaluation each cover one properly. All of them, ours included, quietly assume the corpus holds still while you tune against it. It does not. The wiki page gets edited, the pricing table changes at nine in the morning, a policy is withdrawn and replaced, a customer exercises a deletion right. This article covers that dimension only, and it is architecture-first rather than vendor-first, because the stores and models will turn over several times before the patterns do. The staleness problem,…
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