Fine-tuning was the darling of 2023. Then teams noticed something awkward: updating a fine-tuned model means retraining every time your data changes. RAG means updating your vector store.
The math got undeniable: if your documents change weekly, RAG wins by default. Fine-tuning freezes a snapshot; RAG is a living index.
What RAG does well: grounding in current facts, citing sources, handling internal knowledge without leaking it to a third party.
What RAG doesn't do: teach the model new behaviors or formats. It retrieves text, doesn't change style.
The RAG pipeline matured fast: hybrid search (BM25 + vectors), reranking (Cohere, Voyage, BGE), parent-document retrieval, query rewriting. Naive RAG is dead; engineered RAG is a real discipline.
When RAG fails: bad chunking, bad embeddings, stale index, no reranking. The failures are boring and fixable.
Our take: if you're building an AI feature over internal knowledge, start with RAG, add reranking, evaluate on real questions, then consider fine-tuning only for format consistency.