The question: Should you retrain the model, or give it something to read?
Fine-tuning changes the manner, RAG changes the matter
Both answer the same complaint, “it does not know our data”, but they do not touch the same object. Fine-tuning changes the model; RAG leaves it alone and changes what it has in front of it at the moment of the question.
Two different floors, so two different economics: what is learned is paid for once and corrects badly, what is read is paid for with every request and corrects in a minute.
A price list revised every month, an internal policy, a catalogue.
- BeforeYou gather examples and carry on with the training. The model changes.
- At question timeThe model answers on its own. Nothing is looked up.
- To correct itYou have to train again: the knowledge is diluted, you cannot take out one line.
The sourceNone: there is no way to say where the answer came from.
- BeforeYou prepare the documents and a way of finding them again. The model does not change.
- At question timeThe useful passages are retrieved and placed in the context. Then the model answers.
- To correct itYou edit the document. The next answer is already up to date.
The sourceCitable: you know which passage was used.
What this need calls forRAGInformation that moves belongs in documents. It has to be correctable in a minute and citable if it is challenged, and a training run allows neither.
The day the information changes
Fine-tuning
A new training run
Hours or days, a fixed cost, and no guarantee that the old information comes out cleanly.
RAG
A document edit
A few minutes, immediate effect, and the old version really does disappear.
This is where most projects make up their minds, and rarely at the moment of the initial choice: a system that cannot be corrected in a day ends up not being corrected at all.
Fine-tuning changes the manner of answering, RAG changes the matter you answer about. The question to ask is therefore not “which one is better”, but “is what I want to correct a behaviour or a piece of knowledge”. The two often combine, they never replace one another.
Glossary terms
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