As the applications of large language models expand into specialized domains, the need for efficient and effective adaptation techniques becomes increasingly crucial. Enter RAFT (Retrieval Augmented Fine Tuning), a novel approach that combines the strengths of retrieval-augmented generation (RAG) and
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