RAG vs Traditional Retrieval: When should we rely on Vector DBs for LLM pipelines?

12 Eki 11:45

I've been experimenting with Retrieval‑Augmented Generation and noticed a split in the community: some people swear by dense vector stores for similarity search, while others stick to classic keyword indexes. In theory, vector DBs shine when you need semantic matching across large corpora, but they also bring extra complexity and latency. On the flip side, traditional retrieval is fast and well‑understood but may miss nuanced answers. How do you decide which approach fits a given LLM application? Do you combine both, or pick one based on data size and query type? Would love to hear your experiences and criteria. 🤔