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We propose a natural language prompt-based retrieval augmented generation(Prompt-RAG), a novel approach to enhance the performance of generative largelanguage models (LLMs) in niche domains. Conventional RAG methods mostlyrequire vector embeddings, yet the suitability of generic LLM-based embeddingrepresentations for specialized domains remains uncertain. To explore andexemplify this point, we compared vector embeddings from Korean Medicine (KM)and Conventional Medicine (CM) documents, finding that KM document embeddingscorrelated more with token overlaps and less with human-assessed documentrelatedness, in contrast to CM embeddings. Prompt-RAG, distinct fromconventional RAG models, operates without the need for embedding vectors. Itsperformance was assessed through a Question-Answering (QA) chatbot application,where responses were evaluated for relevance, readability, and informativeness.The results showed that Prompt-RAG outperformed existing models, includingChatGPT and conventional vector embedding-based RAGs, in terms of relevance andinformativeness. Despite challenges like content structuring and responselatency, the advancements in LLMs are expected to encourage the use ofPrompt-RAG, making it a promising tool for other domains in need of RAGmethods.

Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine