  We propose a natural language prompt-based retrieval augmented generation
(Prompt-RAG), a novel approach to enhance the performance of generative large
language models (LLMs) in niche domains. Conventional RAG methods mostly
require vector embeddings, yet the suitability of generic LLM-based embedding
representations for specialized domains remains uncertain. To explore and
exemplify this point, we compared vector embeddings from Korean Medicine (KM)
and Conventional Medicine (CM) documents, finding that KM document embeddings
correlated more with token overlaps and less with human-assessed document
relatedness, in contrast to CM embeddings. Prompt-RAG, distinct from
conventional RAG models, operates without the need for embedding vectors. Its
performance 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, including
ChatGPT and conventional vector embedding-based RAGs, in terms of relevance and
informativeness. Despite challenges like content structuring and response
latency, the advancements in LLMs are expected to encourage the use of
Prompt-RAG, making it a promising tool for other domains in need of RAG
methods.
