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Fashionable IT networks are advanced combos of firewalls, routers, switches, servers, workstations and different gadgets. What’s extra, practically all environments are actually on-premise/cloud hybrids and are always underneath assault by menace actors. The intrepid souls that design, implement and handle these technical monstrosities are referred to as community engineers, and I’m one.

Though different passions have taken me from that world into one other as a start-up founder, a continuing stream of breathless predictions of a world with out the necessity for people within the age of AI prompted me to research, a minimum of cursorily, whether or not ChatGPT could possibly be used an efficient device to both help or ultimately substitute these like me. 

Right here’s what I discovered.

I began by getting the opinion of the very best supply I may consider about how ChatGPT may add worth to community engineers: ChatGPT. It didn’t disappoint and generated an inventory of three areas it decided it may assist:


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  • Configuration administration
  • Troubleshooting
  • Documentation

I then developed a set of prompts — admittedly not optimized — to find out whether or not or not the device may, in truth, be an asset to community engineers in a number of of those areas.

Configuration administration

To check ChatGPT’s means so as to add worth in configuration administration, I submitted the next prompts:

  • Are you able to generate a whole instance configuration for a Cisco router with the aim of beginning an web alternate from scratch?
  • Are you able to create a Jinja template for every vendor?

The ChatGPT outcomes are in depth, so house — and my respect for the boredom limits of these studying this — limits an exhaustive replica of them right here, however I’ve posted the whole transcript of all the ChatGPT prompts and outcomes on GitHub for these looking for a non-pharmaceutical substitute for Ambien.

So, within the case of configuration administration, ChatGPT carried out pretty nicely on primary configuration duties, and I concluded that it’s conscious of vendor-specific syntax and might generate configurations. Nonetheless, the configurations generated by the system needs to be fastidiously inspected for accuracy. The generic prompts I examined can be akin to constructing a fast lab, a activity most younger networking engineers discover tiresome at a minimal and clearly a chore that may be dealt with by the know-how (with, once more, some human oversight).


To check ChatGPT’s prowess at troubleshooting community engineering challenges, I turned to Reddit, and particularly the /r/networking subreddit to seek out real-world questions posed by community engineers to their friends. I pulled a number of questions from the thread and proposed them to ChatGPT with out optimizing the immediate, and the chatbot dealt with the better questions nicely, whereas it struggled with the harder challenges.

Notably, I particularly requested a query that required data of STP, or the Spanning Tree Protocol, a swap functionality chargeable for figuring out redundant hyperlinks that might lead to undesirable loops. Frankly, my opinion is that ChatGPT understands STP higher than many networking professionals I’ve interviewed over time.

At current, ChatGPT can’t substitute skilled networking professionals for even barely advanced points, but it surely wouldn’t be alarmist to recommend that it’d consequence within the obsolescence of many subreddits and Stack Overflow threads within the coming years.

Automating documentation

This was the world of highest deficiency for ChatGPT. The chatbot initially assured me that it may generate networking diagrams. Realizing it’s a text-based device, I used to be clearly skeptical, a prejudice that was confirmed after I requested it to generate a diagram and it defined to me that it doesn’t have graphical functionality.

Additional prompting for community documentation led to the belief — confirmed by ChatGPT — that I wanted to offer an in depth community description for it to offer a community description, clearly not a price add. Thus, within the case of automating documentation, the chatbot not solely failed, however was responsible of producing lies and deception (so maybe it’s nearer to demonstrating human traits than we predict). In equity to AI on the whole, there are AI purposes able to producing pictures, and it’s very doable a type of could also be able to producing a usable community diagram.

I then requested ChatGPT if it may generate a community description primarily based on a router configuration file, and it supplied an honest abstract of what’s configured till it apparently reached the bounds of its computational dedication to my immediate, a restrict possible applied by its designers. It’s, in spite of everything, a free device, and sources are costly, particularly for a company burning significant money lately.


A couple of of the challenges I encountered in my transient experiment when utilizing ChatGPT for community engineering embody:

  • Guaranteeing accuracy and consistency
  • Dealing with edge instances and exceptions
  • Integration with current methods and processes

My guess is these points usually are not distinctive both to ChatGPT or AI purposes typically, and a few cursory analysis might clarify why. Cornell researchers have been learning massive language fashions (LLMs) for a while and “draw a distinction between formal competence — the knowledge of linguistic rules and patterns — and useful competence, a set of expertise required to make use of language in real-world conditions.”

Additionally from a few of their analysis summaries: “Too usually, folks mistake coherent textual content era for thought and even sentience. We name this a “good at language = good at thought” fallacy. Equally, criticisms directed at LLMs middle on their incapacity to assume (or do math or preserve a coherent worldview) and generally overlook their spectacular advances in language studying. We name this a “unhealthy at thought = unhealthy at language” fallacy.

This evaluation is in step with my expertise making ready this text: Specificity reigns supreme in relation to placing ChatGPT to work. Giant, open-ended prompts on advanced matters spotlight a scarcity of “useful competence” within the chatbot, however that actuality doesn’t neutralize its spectacular capabilities when employed for particular duties by a person expert in utilizing it correctly.

So, can ChatGPT substitute community engineers?

Not but.

Mike Starr is the CEO and founding father of trackd.


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