[adampk]

> Retrieval Aware Fine-Tuning (RAFT), presents a novel recipe to prepare fine-tuning data to tailor the models for domain-specific open-book setting, equivalent to in-domain RAG.

I cannot see the insight on why this is a for a limited domain? The key problem that is being solved is the known problem where RAG returns an irrelevant chunk. It seems like the "benefit" is training a model to ignore irrelevant chunks.

I am guessing because it costs money to train on multi-domains so they limited their research on one-domain at a time but not sure if there is a "bigger reason" why this isn't an approach to a fine-tuned "make answers from only relevant chunks" model? The paper seems to imply this is only works for specific-domains but I can't see why.