The modern IT network is a complex combination of various devices such as firewalls, routers, switches, servers, and workstations. These environments are constantly under threat by malicious actors, requiring network engineers to design, implement and manage them. However, the rise of AI has prompted exploration into whether ChatGPT could assist or even replace network engineers. This article explores ChatGPT’s ability to add value to network engineering in three areas: configuration management, troubleshooting, and documentation.
Configuration Management
To determine ChatGPT’s ability to add value in configuration management, a set of prompts was developed, asking whether it could generate a complete example configuration for a Cisco router, Juniper, and create a Jinja template for each vendor. The results were extensive and showed that ChatGPT performed well on basic configuration tasks and is aware of vendor-specific syntax. However, the configurations generated by the system should be carefully inspected for accuracy. The generic prompts tested would be akin to building a quick lab, a task most young networking engineers find tiresome at a minimum and clearly a chore that can be handled by the technology (with, again, some human oversight).
Troubleshooting
To test ChatGPT’s ability to troubleshoot network engineering challenges, real-world questions posted by network engineers to their peers on the /r/networking subreddit were used. The chatbot handled easier questions well, while struggling with more difficult challenges. Notably, a question was asked that required knowledge of STP, or the Spanning Tree Protocol, a switch capability responsible for identifying redundant links that could result in unwanted loops. ChatGPT understood STP better than many networking professionals. However, it cannot replace experienced networking professionals for even slightly complex issues, but it wouldn’t be alarmist to suggest that it might result in the obsolescence of many subreddits and Stack Overflow threads in the coming years.
Documentation
In the case of automating documentation, the chatbot failed to add value, as it could not generate networking diagrams or a network description. Further prompting for network documentation led to the realization that a detailed network description was required. Thus, the chatbot not only failed but was guilty of generating lies and deception. In fairness to AI in general, there are AI applications capable of generating images, and it’s very possible one of those may be capable of producing a usable network diagram.
Challenges
A few challenges were encountered when using ChatGPT for network engineering, including ensuring accuracy and consistency, handling edge cases and exceptions, and integration with existing systems and processes. These issues are not unique to ChatGPT or AI applications generally.
Specificity reigns supreme when it comes to putting ChatGPT to work. Large, open-ended prompts on complex topics highlight a lack of “functional competence” in the chatbot, but that reality doesn’t neutralize its impressive capabilities when employed for specific tasks by an individual skilled in using it properly. While ChatGPT has shown potential in assisting network engineers in configuration management and troubleshooting, it cannot replace experienced professionals for even slightly complex issues. However, with further advancements in AI, it’s possible that ChatGPT and other AI applications could eventually contribute to the obsolescence of certain network engineering tasks.
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