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Chatbot System Design Using RAG: Key Components and Considerations
As conversational AI continues to evolve, Retrieval-Augmented Generation (RAG) has become an increasingly popular architecture for creating sophisticated, context-aware chatbot systems. RAG enhances chatbot performance by blending Large Language Models (LLMs) with contextually relevant retrieval from a knowledge base. This hybrid approach ensures the chatbot not only generates coherent responses but also provides accurate, real-time information.
In this article, we’ll explore a highly scalable and reliable system design for a chatbot based on RAG. We’ll break down the architecture into its essential components, explaining their roles and considerations, and showing how this architecture meets the functional and non-functional requirements for real-world AI applications. By the end, you’ll have a clearer picture of how to design such a system, especially if you’re preparing for AI system design interviews.
Key Components of a RAG-based Chatbot System
The architecture we’ll discuss consists of several core components, each integral to the system’s functionality. These components work together to ensure the chatbot is responsive, scalable, and reliable while delivering accurate, contextually relevant responses to user queries.
