RAG is a pattern where you search your own documents for relevant passages and paste them into the prompt, so the model answers from real sources instead of memory.
RAG supplies knowledge at request time. Fine-tuning changes the model's behaviour. Use RAG for facts that change, fine-tuning for consistent style.
Two ways to give a model information it was not trained on. Long context puts everything in the prompt. RAG retrieves only what is relevant.
Retrieval augmented generation, or RAG, is a pattern where you look up relevant documents first and put them into the prompt. The model then answers from that material instead of from memory.
It fixes two things at once. The model can use information it was never trained on, including data from this morning, and it can cite where an answer came from.
Think of it like this. Think of an open-book exam. The student is the same. The difference is that the right pages are open in front of them, so they quote instead of recall.