  The paper presents a methodology for uncovering knowledge gaps on the
internet using the Retrieval Augmented Generation (RAG) model. By simulating
user search behaviour, the RAG system identifies and addresses gaps in
information retrieval systems. The study demonstrates the effectiveness of the
RAG system in generating relevant suggestions with a consistent accuracy of
93%. The methodology can be applied in various fields such as scientific
discovery, educational enhancement, research development, market analysis,
search engine optimisation, and content development. The results highlight the
value of identifying and understanding knowledge gaps to guide future
endeavours.
