RAG Explained: Understanding Embeddings, Similarity, and Retrieval

The article explains the retrieval-augmented generation (RAG) framework, emphasizing how it combines large language models with retrieval systems to enhance information accuracy and relevance. Central to this approach are embeddingsvector representations of dataand similarity measures that enable efficient retrieval of relevant documents from large datasets, thereby improving the model's ability to generate contextually accurate responses.