Eclectic-AI

What is Retrieval Augmented Generation? Generative AI is currently garnering lots of attention. While the responses provided by the large language models (LLMs) are satisfactory in most situations, sometimes we want to get better focused responses when employing LLMs in specific domains. Retrieval-augmented generation (RAG) offers one such way to improve the output of generative AI systems. RAG enhances the LLMs capabilities by providing them with additional knowledge context through information retrieval. Thus, RAG aims to combine the strengths of both retrieval-based methods, which focus on selecting relevant information, and generation-based methods, which produce coherent and fluent text.  RAG works in the following way: Retrieval : The process starts with retrieving relevant documents, passages, or pieces of information from a pre-defined corpus or database. These retrieved sources contain content that is related to the topic or context for which you want to gen