US Update: What is RAFT? RAG + Fine-Tuning

In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance generative responses from a large language model for specific applications in that particular domain.It allows the large language models to provide more accurate, contextually relevant, and robust results, especially for targeted sectors like healthcare, law, and finance, by integrating RAG and fine-tuning.Components of RAFT1.

Retrieval-augmented GenerationThe technique enhances LLMs by permitting them to access external data sources during inference.

Therefore, rather than static pre-trained knowledge as with many others, RAG enables the model to actively search a database or knowledge repository for information within two clicks to respond to user queries.

It is almost like an open-book exam, in which the model consults the most recent external references or other domain-relevant facts.

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