RAG is much closer to how the human brain works than fine tuning a model is. Arguably, RAG points to the existence or the beginnings of AGI. The computer can take new information and use it to inform their answer.
I disagree with that. The model isn't fetching information to augment its responses, nor does it have any notion of ground truth or accuracy. I would argue that RAG points at the inverse - we have these incredible probabilistic autocomplete machines but they're so incapable of being reliable that we have to force them to work in a context that we establish for them.
How does it decide which information to request? How does it know which is the right or wrong information for a given request? Maybe it's possible to wave this away with "something in the future will do that!" but that's literally the entire reason why all current AI is limited. It doesn't have the business context or reasoning capabilities to make the right decisions to decide what the right data to augment generation even is. I don't believe we're any closer to AGI.
I think LLMs are fairly good at deciding what information is relevant to a query or not though, it is rare that I have found an LLM to be distracted by RAG results (even when they're not relevant)
So have it be a separate module? The human brains is compartmentalized too.
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Obviously, the human brain continuously integrates new material, but whether that's more like RAG, continuous fine-tuning, just having a really big context window, or just having a completely different model architecture where activiation (or not) of pathways in inference also produces weight adjustments directly — or doesn't meaningfully work like any of those — is another question, where the answer is less obvious.
I think RAG (storing exact data losslessly with some sort of indexing to allow retrieval, and something hidden-prompt-like directing the use of that in populating an inference context) is a lot less likely that something that involves dynamically adjusting model weights. Which also explains forgetting, not just learning.
I see these two claims as orthogonal. AGI need not work like the human brain, and I’m not sure people’s brains have any lossless representation of memories like a RAG system would. People are infamously unreliable when it comes to recalling information during crimes for example.
However, it does seem like a RAG system is much more versatile than continuously training. Facts change all the time and it’s infeasible to retrain every time that happens.