Transforming raw corporate texts into instruction dataset for fine-tuning generator within a RAG system

This paper describes a method for constructing an instruction dataset for fine-tuning a large language model (LLM) to serve as a generator within a retrieval-augmented generation (RAG) pipeline. The practical implementation of this method is demonstrated through the construction of a dataset tailored for fine-tuning the generator of a corporate intelligent assistant based on the RAG architecture.