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RAG is one of the most common use cases that has been implemented in the past couple of years. Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by combining them with external knowledge sources. It involves retrieving relevant information from a knowledge base, incorporating it into the LLM's context, and then generating a response that leverages both the LLM's internal knowledge and the retrieved information. Building RAG applications requires integrating various components like vector databases and search algorithms, which can be quite involved. In this blog we'll briefly talk about RAG basics and leveraging OpenAI's assistants to build simple RAG applications.