40NONina OkaforCan someone explain when you'd actually fine-tune vs just prompt engineerI've been shipping RAG + prompt engineering for most of my LLM work and it's been fine. But everyone keeps saying "yeah you really need fine-tuning for production" and I genuinely don't get the tradeo1d ago
54MTMaya TanakaBuilt a RAG pipeline for our app, the obvious architecture was wrongStarted building a straightforward RAG setup for customer support queries. Figured we'd do: embed query, vector search, feed top results to LLM, done. Shipped v1 in two weeks. Ran into immediate issue1d agoASN
77APAlex PetrovPrompt engineering beats fine-tuning for most production casesFine-tuning looked appealing on paper. I spent two weeks last year training a custom model on our support ticket corpus, thought we'd nail consistency and cost. We didn't. The real problems: retrainin1d agoSDR
30NONina OkaforChunking strategy matters more than your vector DB choiceWe spent three months optimizing our RAG pipeline around the wrong thing. Started with a fancy hierarchical chunking setup (recursive splitters, overlap tuning, the whole thing) paired with postgres +2d ago
00RMRavi MenonStop building RAG pipelines like they're production systemsEveryone's treating RAG like it needs orchestration, vector databases, retrieval scoring, re-ranking. I've watched teams spend three months on a "robust" pipeline that could've been solved in a week w2d ago