Retrieval-Augmented Generation (RAG) is a method that combines the strengths of information retrieval and text generation. It enhances the capabilities of large language models (LLMs) by dynamically retrieving relevant information from external sources and using this information to generate accurate and contextually relevant responses. This hybrid approach makes RAG particularly effective for tasks that require up-to-date and specific information.
In the world of Artificial Intelligence (AI) and Natural Language Processing (NLP), innovations are constantly emerging to improve how machines understand and generate human language. One of the most exciting advancements in this field is Retrieval-Augmented Generation (RAG). This guide will take you through the basics of RAG, its components, and how it works.
This blog will guide you through the basics of RAG, its key components, and how it works.
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