Your comment really helps me improve my mental model about LLM. Can someone smarter help me verify my understanding:

1) at the end of the day, we are still sending raw text over LLM as input to get output back as response.

2) RAG/Embedding is just a way to identify a "certain chunk" to be included in the LLM input so that you don't have to dump the entire ground truth document into LLM Let's take Everlaw for example: all of their legal docs are in embeddings format and RAG/tool call will retrieve relevant document to feed into LLM input.

So in that sense, what do these non-foundational models startups mean when they say they are training or fine tuning models? Where does the line end between inputting into LLM vs having them baked in model weights