RAG vs. fine-tuning
For 9 out of 10 business assistants, the answer is RAG. RAG searches your documents on every question and hands the model the context — you update the content, not the model. Fine-tuning changes the model's behaviour itself: useful for tone, format or repetitive tasks, useless for facts that change. The cost follows the same split: RAG is content, fine-tuning is training.
RAG changes what the model KNOWS at question time; fine-tuning changes HOW it behaves. Facts → RAG. Behaviour → fine-tuning.
RAG
Retrieval-Augmented GenerationA technique where, before answering, the assistant searches the relevant fragments from your documents and hands them to the model as context. The answer is thus anchored in real data, not in the model's generic memory — the primary method for reducing hallucinations.
You have documents, prices, a catalogue or policies that change. You want verifiable, sourced answers without retraining on every update.
Fine-tuning
Retraining a model on your own examples so it adopts a specific style, format or behavior. Unlike RAG (which adds knowledge at answer time), fine-tuning changes the model itself. Useful for tone and repetitive tasks; more expensive to maintain.
You need a very specific tone or format, repeated at scale, and the system prompt is no longer enough.
This comparison is part of the Atlas — Websem's reference of AI search, Google Ads, tracking and chatbot terms. 129 terms, each with its own definition.