I’ve been reading about RAG pipelines and there’s this recurring assumption that managing your own vector store is standard practice. But when you’re building RAG workflows visually in Latenode without touching vector database management, I realized I don’t actually understand what I’m losing.
Like, the system handles document processing and retrieval automatically. It can connect to my knowledge base, pull context, and feed it to generation models. But I keep wondering—what visibility or control am I giving up by not manually managing embeddings, chunk sizes, retrieval thresholds, or vector similarity tuning?
Is it just convenience? Or are there legitimate RAG use cases where you actually need direct vector store control? And if I do hit limitations, what does that usually look like?