How to Use Retrieval-Augmented Generation (RAG) Locally

In this blog post, we'll explore how to use Retrieval-Augmented Generation (RAG) for building more effective and engaging conversational AI applications. We'll cover the basics of RAG, its benefits, and provide step-by-step instructions on how to develop your own RAG mechanism for local use. What Is RAG? RAG (Reinforcement-based Generation) combines the strengths of two prominent approaches in natural language processing (NLP): retrieval-based models and generation-based models. In traditional generation-based methods, AI systems generate text from scratch using pre-trained patterns and rules. However, this approach often leads to limited creativity, a lack of context-specific knowledge, and poor coherence.