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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.19627 |
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Table of Contents:
- This paper reports on a real-world case study in which over 100 network engineers assessed how a Large Language Model (LLM) can assist in building and operating a network. The versatility of LLMs has accelerated their adoption across a wide range of domains, and assisting network operations is one such promising application. LLMs are probabilistic models, unlike deterministic protocols and configurations; therefore, clarifying their capabilities -- how and to what extent LLMs can help in network operations -- is a crucial step toward adopting LLMs. To offer practical insights into this issue, we conducted an extensive experiment on a large demonstration network built for a public exhibition, consisting of 21 racks with heterogeneous network devices. In the experiment, a total of 105 network engineers used an LLM-based chatbot while building and operating the network. The chatbot was equipped with three external functions: retrieval-augmented generation for domain-specific knowledge, CLI control of network devices running on the network, and access to a ticket system. The participants gave evaluations for the chatbot's responses on a best-effort basis. Analysis of the chat histories shows that 68.1% of the evaluations were positive, indicating a quantitative baseline of the LLM's helpfulness in network operations. Our results also demonstrate that understanding the capabilities of the chatbot is important for eliciting better responses. Moreover, we provide detailed use case analyses while sharing actual user--chatbot interactions.