KAT: Knowledge-Context Augmentation for Evolving LLM-Based Telecom Troubleshooting

Abstract

Telecom Business Support Systems (TBSS) are critical to the operation and maintenance (O&M) of modern telecommunications networks. However, troubleshooting in TBSS is challenging due to diverse error descriptions, dependencies across multiple subsystems, and the constant emergence of new errors. Directly applying large language models (LLMs) to troubleshoot such ill-defined system problems remains difficult. This paper designs a knowledge-context augmented framework, KAT, that combines structured knowledge, context enhancement, and continual learning to unlock LLMs’ troubleshooting capabilities. Our KAT has been deployed in a commercial TBSS serving over 35 million users, handling more than 80k system errors monthly. It has also been successfully scaled to commercial TBSSs in 15 additional cities, automatically resolving 10k–20k system errors per month. After 10 months of testing, KAT achieves an average BERT-F1 of 86.53%, improves the monthly user satisfaction rate from 47% to 97%-99%, and reduces the average error duration from 51.4 to 5.9 hours, saving troubleshooting labor and improving user experience significantly.