"Can we just train the model on this data together?" is one of the most common things people ask an AI assistant. And al...

"Can we just train the model on this data together?" is one of the most common things people ask an AI assistant. And almost every time, the answer comes back: "Let's use RAG instead." That answer sounds like a dodge if you don't know what's actually happening underneath it. It isn't. Training and RAG solve genuinely different problems, and once you see the mechanism behind each one, the recommendation makes complete sense.This article breaks down what training actually does to a model, what RAG actually does (spoiler: it doesn't touch the model at all), and why the two aren't interchangeable — plus when fine-tuning genuinely is the right call. What Training Actually DoesA language model is, at its core, a huge set of numbers called weights — often billions of them. Training is the process of adjusting those weights so the model gets better at predicting the next piece of text.There are two stages worth separating:Pretraining is where a model learns language, reasoning patterns, and ge...