Retrieval Augmented Generation (RAG) enhances the performance of large language models (LLMs) by incorporating extra knowledge from an external data source, which wasn't involved in the original model training. The two main components of RAG include indexing and retrieval.
Despite their merits, pre-trained embeddings models, trained on generic datasets like Wikipedia, often struggle to effectively portray…
