RAG pipelines vs. traditional retrieval: Should vector databases be the default backbone?

02 Eyl 21:00

Retrieval‑augmented generation relies heavily on the underlying store for retrieved passages. With the rise of dense embeddings, many projects now default to a vector database for similarity search, while others still prefer classic inverted indexes or hybrid solutions. I'm curious how the community balances factors like latency, scaling costs, and the ability to incorporate metadata filters. Do you think pure vector stores are mature enough to be the default backbone for RAG, or should they be combined with traditional term‑based retrieval for robustness? Also, how do you handle frequent index updates without sacrificing query speed? Would love to hear your architectures and any pitfalls you've encountered.