A Primer on Generative AI for Telecom: From Theory to Practice

TL;DR

This paper explores the application of generative AI, especially large language models and retrieval augmented generation, in telecom, highlighting practical use cases like chatbots and providing insights from theory to real-world deployment.

Contribution

It offers a comprehensive overview of GenAI in telecom, emphasizing RAG techniques and presenting a publicly accessible O-RAN chatbot as a practical example.

Findings

RAG enhances LLM accuracy with telecom data sources

The O-RAN RAG chatbot demonstrates industry interest and practical viability

GenAI models improve telecom customer service and innovation

Abstract

The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively. We highlight the importance of retrieval augmented generation (RAG) in connecting LLMs to telecom domain specific data sources to enhance the accuracy of the LLMs' responses. We present a real-world use case on RAG-based chatbot that can answer open radio access network (O-RAN) specific questions. The demonstration of the chatbot to the O-RAN Alliance has triggered immense interest in the industry. We have made the O-RAN RAG chatbot publicly accessible on GitHub.