Join top executives in San Francisco on July 11-12 to hear how leaders are integrating and optimizing AI investments for success. Learn more
Modern IT networks are complex combinations of firewalls, routers, switches, servers, workstations and other devices. In addition, almost all environments are now on-premise/cloud hybrids and are under constant attack by threat actors. The intrepid souls who design, implement and manage these technical monstrosities are called network engineers, and I am one.
While other passions have taken me from that world to another as a startup founder, a constant stream of breathless predictions of a world without humans in the age of AI led me to explore, at least in passing, whether ChatGPT could become used an effective tool to help or eventually replace those like me.
This is what I discovered.
I started by getting the opinion of the best resource I could think of on how ChatGPT could add value to network engineers: ChatGPT. It didn’t disappoint, generating a list of three areas it determined could help:
- Configuration management
- Fix the problem
I then developed a series of prompts – admittedly not optimized – to determine whether the tool could in fact be an asset to network engineers in one or more of these areas.
To test ChatGPT’s ability to add value in configuration management, I submitted the following prompts:
- Can you generate a complete example configuration for a Cisco router with the aim of starting an internet exchange from scratch?
- Can you make a Jinja template for each supplier?
The ChatGPT results are extensive, so space – and my respect for the limits of boredom of those reading this – limits an exhaustive reproduction of them here, but I’ve posted the full transcript of all ChatGPT prompts and results at GitHub for those looking for a non-pharmaceutical alternative to Ambien.
So in the case of configuration management, ChatGPT performed quite well on basic configuration tasks, and I concluded that it knows about vendor-specific syntax and can generate configurations. However, the configurations generated by the system must be carefully inspected for accuracy. The generic prompts I tested would be akin to building a high-speed lab, a task most young network engineers find tiresome to say the least and clearly a chore the technology can handle (with, again, some human oversight) .
Fix the problem
To test ChatGPT’s prowess at solving network engineering challenges, I turned to Reddit, and specifically the /r/networking subreddit, to find real questions posed by network engineers to their peers. I pulled a few questions from the thread and submitted them to ChatGPT without optimizing the prompt, and the chatbot handled the easier questions well while struggling with the more difficult challenges.
Notably, I specifically asked a question that required knowledge of STP or the Spanning Tree Protocol, a switching function responsible for identifying redundant links that could result in unwanted loops. Honestly, I believe ChatGPT understands STP better than many networking professionals I’ve interviewed over the years.
At this point, ChatGPT can’t replace experienced network professionals for even slightly complex issues, but it wouldn’t be alarming to suggest that it could lead to the obsolescence of many subreddits and Stack Overflow threads in years to come.
This was the area of greatest shortcoming for ChatGPT. The chatbot initially assured me that it could generate network diagrams. Knowing it’s a text-based tool, I was obviously skeptical, a preconceived notion that was confirmed when I asked it to generate a chart and it explained to me that it has no graphics capabilities.
Further queries for network documentation led to the realization – confirmed by ChatGPT – that I had to provide a detailed network description to provide a network description, clearly no added value. So in the case of automating documentation, the chatbot not only failed, but was also guilty of generating lies and deceit (so maybe it’s closer to demonstrating human characteristics than we think). To be fair to AI in general, there are AI applications that can generate images, and it’s quite possible that one of them can produce a usable network diagram.
I then asked ChatGPT if it could generate a network description from a router configuration file, and it provided a decent summary of what was configured until it apparently hit the limits of its computational effort for my prompt, a limit probably implemented by the designers . After all, it is a free tool and resources are expensive, especially for an organization burning a lot of money these days.
Some of the challenges I encountered in my short experiment using ChatGPT for network engineering are:
- Ensuring accuracy and consistency
- Handling peripheral cases and exceptions
- Integration with existing systems and processes
I suspect these issues aren’t unique to ChatGPT or AI applications in general, and some cursory research may explain why. Researchers at Cornell have been studying large language models (LLMs) for some time and “distinguish between formal competence — the knowledge of linguistic rules and patterns — and functional competence, a set of skills needed to use language in real-world situations.
Also from some of their research abstracts: “Too often people confuse coherent text generation with thinking or even feeling. We call this a “good at language = good at thinking” fallacy. Similarly, criticism of LLMs centers on their inability to think (or do math or maintain a coherent worldview) and sometimes overlook their impressive progress in language learning. We call this a “bad at thinking = bad at language” fallacy.
This analysis is consistent with my experience preparing this article: specificity reigns when it comes to putting ChatGPT to work. Large, open-ended prompts on complex topics indicate a lack of “functional competence” in the chatbot, but that reality doesn’t negate its impressive capabilities when used for specific tasks by a person skilled in its proper use.
So, can ChatGPT replace network engineers?
Mike Starr is the CEO and founder of followed.
Data decision makers
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovation.
To read about advanced ideas and up-to-date information, best practices and the future of data and data technology, join DataDecisionMakers.
You might even consider contributing an article yourself!
Read more from DataDecisionMakers