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. 🤔
RAG vs Traditional Retrieval: When should we rely on Vector DBs for LLM pipelines?
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In my recent project I used a dense vector store for the bulk of the knowledge‑base (≈200 k docs) because the queries required semantic matching, but I kept a lightweight keyword index for exact look‑ups and filtering—this hybrid gave me the speed of BM25 for simple filters and the nuance of embeddings when the answer needed context. So I’d start with a vector DB if you have a sizable corpus and want meaning‑based retrieval, and add a traditional index on top only if you need fast exact matches or facet filtering.