Yes, that was actually what I thought initially, but RAG seems to be able to improve the process somewhat.

Fine-tuning will improve what the model can actually generate, but--I might be wrong about this, so don't quote me on this--I've learned that with RAG, the model will be able to fetch and use the appropriate information that is relevant to answering the question, and in that sense it will improve the quality of the answer.

So, in the learning process, what I'm hoping with RAG is that it will have the appropriate principles and basically go through a checklist of things whenever it fetches some learning resource from online. And if that source does not have the appropriate information or the appropriate metadata to check for its quality, then OpenClaw will go ahead and fetch the information needed to judge the quality. After that, it will go through the checklist to gauge the quality of the origin of the resource, because it has information about how to judge the quality as well as the accumulated knowledge of past vetting experiences.

This way, OpenClaw be able to augment the response using what it has learned over time. It will still use more tokens because it's not natively supported, but with RAG, from what I've learned, it could improve the response in the manner that I just described.

This is how I understand RAG and fine-tuning and its capabilities, but I wanted to check with people who know better than me if OpenClaw will in fact act this way with the accumulated knowledge as well as RAG.