One of the most impactful applications of generative AI for businesses is to create natural language interfaces that have access to existing knowledge. This means answering questions about specific domains such as banking, legal and medical. There are currently two main ways to do this. First: domain-specific Fine-tuning (DSF), which means training an existing base model on a set of documents that represent the domain specific knowledge. Second: RAG (Retrieval Augmented Generation), which involves storing those documents in a vector database and (at query time) finding documents based on their semantic similarity with the question and bringing them into the context of the LLM for in context learning.
In this article, we will look at the limitations of those two approaches and how a team of UC Berkeley researchers, Tianjun Zhang and Shishir G. Patil, may have just discovered a better approach. The team previously known for Gorilla LLM :gorilla: presents this new approach in their RAFT paper (Retrieval Augmented Fine Tuning) showing how they used Meta Llama 2 and Azure AI Studio to conduct their research and implement their approach.

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