Next-Generation Networking: Enhancing Intent-Based Architectures with Large Language Models and Retrieval-Augmented Generation

As current networks become increasingly complex and dynamic, traditional network management approaches struggle to keep pace with the evolving demands for flexibility and automation. Intent-Based Network (IBN) promises to bridge this gap by translating user intents into network configurations automatically. However, current IBN frameworks often fall short in understanding nuanced user intents and context, limiting their potential in dynamic environments. This paper introduces an innovative architecture that enhances IBN with the cognitive capabilities of Large Language Models (LLMs) and the contextual precision of Retrieval-Augmented Generation (RAG). Our proposed architecture leverages LLMs to process natural language intents comprehensively, while RAG provides a mechanism to enrich these interpretations with context-specific information retrieved from vast knowledge bases instead of a static knowledge base. Together, they form a robust system capable of generating accurate, dynamic network policies and configurations in real time. We detail the system design and implementation, which includes modules for natural language understanding, decision-making, policy management, and network orchestration. This approach not only streamlines network management but also aligns it closely with organizational goals and real-world conditions, paving the way for next-generation networking solutions.