AI for Network Engineers: Automation, Troubleshooting & Ops

About this course

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Network engineers have always automated themselves out of repetitive work — from typing commands, to Expect scripts, to Jinja2 templates, to model-driven configuration. Large Language Models are the next layer in that same line, and this course teaches you to use them as a serious working tool: not a chatbot novelty, but a co-worker that reads logs faster than you can, drafts configuration in your house style, and turns a wall of show output into a plain-English answer — without ever letting a probabilistic tool make an unsupervised change to your network.

This is a hands-on, code-first course. Almost every lecture ends in something you can actually run, and the six sections build toward three complete, deployable projects. You will work with both Cisco IOS/NX-OS and Juniper Junos throughout, because to a language model both are simply text, and most real networks are mixed. Every script is written so you can point it at a cloud API (Claude/OpenAI-compatible) or a local model running on your own hardware via Ollama by changing a single line.

What the course covers, section by section:

Foundations — what actually changed with LLMs, tokens and context windows, why models hallucinate (and the five levers that stop it), setting up a reusable Python toolkit, and writing prompts that reliably return clean, structured JSON.Core Use Cases — generating and validating configuration with a safe apply loop (diff, dry-run, human gate), filtering and triaging syslog at scale, running a stateful troubleshooting co-pilot grounded in live device output, automating documentation and diagrams-as-code, and getting an AI-assisted second opinion on a network design.

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