♻ RAG (Retrieval Augmented Generation) is a process that enhances the relevance and accuracy of an LLM’s output by dynamically retrieving relevant information from external or private data sources and incorporating it into the generation process. This allows the model to make more informed, context-aware, and domain-specific responses—making it especially valuable in scenarios where up-to-date or proprietary knowledge is critical.
💡 Think of RAG as a bridge between your private data and the LLM’s generative capabilities—enabling smarter, more reliable outputs without needing to retrain the model.