Retrieval Augmented Generation (RAG) is a system that enhances the accuracy of AI models by integrating external data sources, addressing the challenge of outdated information in language models like Llama 3 and GPT-4. DocsGPT exemplifies this approach by creating an AI chatbot that uses RAG to incorporate real-time knowledge from external documents, such as blog posts or product documentation, which are uploaded to a vector store and accessed during user interactions. Built with technologies like Next.js, TypeScript, and OpenAI’s Assistants API, DocsGPT features a full-stack architecture that includes authentication and authorization managed by Descope, ensuring secure access to its functionalities. The chatbot addresses the recency issue prevalent in developer-facing companies by accurately handling inquiries about new product releases and documentation updates, thus mitigating the hallucination problem often encountered with generalist chatbots. DocsGPT is open-source, allowing developers to explore its implementation and customize it for similar use cases, while also highlighting the significance of identity management and role-based access control in modern applications.