[l72]

I've described it this way to my colleagues:

RAG is a bit like having a pretty smart person take an open book test on a subject they are not an expert in. If your book has a good chapter layout and index, you probably do an ok job trying to find relevant information, quickly read it, and try to come up with an answer. But your not going to be able to test for a deep understanding of the material. This person is going to struggle if each chapter/concept builds on the previous concept, as you can't just look up something in Chapter 10 and be able to understand it without understanding Chapter 1-9.

Fine-tuning is a bit more like having someone go off and do a phd and specialize in a specific area. They get a much deeper understanding for the problem space and can conceptualize at a different level.

[CGamesPlay]

What you said about RAG makes sense, but my understanding is that fine-tuning is actually not very good at getting deeper understanding out of LLMs. It's more useful for teaching general instructions like output format rather than teaching deep concepts like a new domain of science.

[bashfulpup]

This is true if you don't know what you're doing, so it is good advice for the vast majority.

Fine tuning is just training. You can completely change the model if you want make learn anything you want.

But there are MANY challenges in doing so.

[CGamesPlay]

This isn't true either, because if you don't have access to the original data set, the model will overfit on your fine tuning data set and (in the extreme cases) lose its ability to even do basic reasoning.

[DebtDeflation]

Yes. It's called "catastrophic forgetting". These models were trained on trillions of tokens and then underwent a significant RLHF process. Fine tuning them on your tiny data set (relative to the original training data) almost always results in the model performing worse at everything else. There's also the issue of updating changed information. This is easy with RAG - replace the document in the repository with a new version and it just works. Not so easy with fine tuning since you can't identify and update just the weights that were changed (there's research in this area but it's early days).

[bashfulpup]

Again, that's why I said it is challenging.

I regularly do fine tuning on a model with fine results and little damage to the base functionality.

It is possible, but it's too complex for the majority of users. It requires a lot of work per dataset you want trained on.