  The integration of Machine Learning and Artificial Intelligence (ML/AI) into
fifth-generation (5G) networks has made evident the limitations of network
intelligence with ever-increasing, strenuous requirements for current and
next-generation devices. This transition to ubiquitous intelligence demands
high connectivity, synchronicity, and end-to-end communication between users
and network operators, and will pave the way towards full network automation
without human intervention. Intent-based networking is a key factor in the
reduction of human actions, roles, and responsibilities while shifting towards
novel extraction and interpretation of automated network management. This paper
presents the development of a custom Large Language Model (LLM) for 5G and
next-generation intent-based networking and provides insights into future LLM
developments and integrations to realize end-to-end intent-based networking for
fully automated network intelligence.
