This reminds me of finetuning LLMs vs using RAG:

In RAG you "know" what the model knows and it's easy for it to give sources - you literally give it to the model in the prompt.

In finetuning, the model learns something, but it might not be able to reproduce it perfectly later on. But it's models have been changed.