RAG Series: Part 1

Retrieval Augmented Generation (RAG) is a technique that combines retrieval of external information with a language model's ability to generate text. In this approach, when a user poses a question, the system retrieves relevant information from a large knowledge base. This retrieved knowledge together with the user query is then provided to an LLM, which uses this context to generate an informed response. The retrieval component is crucial as it addresses the challenge of identifying useful information from a vast dataset, typically using similarity searches based on vector embeddings.