AI Networking Cookbook

AI for Network Engineers, Explained Through Real Recipes

AI Networking Cookbook is built for readers who want to put AI to work in networking tasks, not just talk about it. Eric Chou approaches the subject as an engineer: with practical examples, guided experiments, and a focus on how large language models can support automation, troubleshooting, and application development in a networking environment.

The book opens with the AI LLM landscape and the key parameters that shape model behavior, then moves into hands-on work with OpenAI and local tools such as Ollama. From there, it develops into more applied networking workflows, including device-aware prompting, AI-assisted application backends, and network monitoring use cases. The structure makes it clear that this is a working cookbook for practitioners who learn best by doing.

What the Book Emphasizes

• Using AI tools in a disciplined, engineering-focused way
• Building practical familiarity with LLMs and prompt design
• Connecting AI techniques to network automation workflows
• Exploring both cloud-based and local model options

Who Will Appreciate It

This title is a strong fit for network engineers, automation developers, and technically comfortable readers who want to understand how AI can fit into everyday networking work. It should also appeal to readers who prefer structured, recipe-based learning and want a book that moves quickly from concepts into implementation.

A Practical Starting Point for AI-Assisted Networking

If you are looking for a network-focused guide that treats AI as a working tool rather than a buzzword, this ebook offers a focused and current entry point. The emphasis on real recipes makes it easy to browse chapter by chapter or follow the material sequentially as your own AI-assisted workflows take shape.