Understand RAG and its importance in providing context to LLMs

Retrieval-Augmented Generation (RAG) enhances the capabilities of Large Language Models (LLMs) by providing contextual information during prompt generation, thereby improving the accuracy and relevance of their responses. Unlike relying solely on vector search, which focuses on semantic similarity, RAG emphasizes the importance of defining and retrieving the appropriate context for each task to simulate memory and knowledge in otherwise stateless LLM interactions. By expanding variables in prompts with retrieved data, RAG allows LLMs to incorporate relevant external information such as customer profiles or domain-specific documentation to better answer queries. While vector search can be a component of RAG, especially for finding similar content, the primary focus should be on how to effectively retrieve and represent context in text form to enhance the LLM's performance.