  Large language models (LLMs) are rapidly emerging in Artificial Intelligence
(AI) applications, especially in the fields of natural language processing and
generative AI. Not limited to text generation applications, these models
inherently possess the opportunity to leverage prompt engineering, where the
inputs of such models can be appropriately structured to articulate a model's
purpose explicitly. A prominent example of this is intent-based networking, an
emerging approach for automating and maintaining network operations and
management. This paper presents semantic routing to achieve enhanced
performance in LLM-assisted intent-based management and orchestration of 5G
core networks. This work establishes an end-to-end intent extraction framework
and presents a diverse dataset of sample user intents accompanied by a thorough
analysis of the effects of encoders and quantization on overall system
performance. The results show that using a semantic router improves the
accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with
prompting architectures.
