AI Networking Cookbook
Practical Recipes for AI-Assisted Network Automation and Development
Will be published approx. on 14. January 2026
Book
Paperback/Softback
346 pages
978-1-80580-799-5 (ISBN)
Description
Transform your network operations with AI-powered automation and learn code generations, prompt engineering, and practical recipes for custom network tools using AI assistants and Python
Key Features
Leverage AI assistants like OpenAI and Claud to build network automation solutions
Use prompt engineering and AI tools to automate network setup, monitoring, and threat detection
Build AI-assisted network configuration, monitoring, and management with multi-vendor APIs
Book DescriptionTransform your approach to network automation with the power of AI LLM assistants. This cookbook gives you practical, hands-on recipes for rapidly and efficiently building custom automation solutions with AI.
You'll discover how to apply tools such as Vibe coding for conversational development, OpenAI API scripts, prompt engineering for better outputs, fine-tuning local LLMs, combining models with LangChain, and creating rapid frontends with Streamlit.
Across all the chapters, you'll progress from simple Python scripts to advanced AI-assisted automation techniques, including multi-vendor API integration. Along the way, you'll learn how to harness AI for network configuration, monitoring, security, and troubleshooting.
Each recipe includes realistic mock data, complete code examples, and step-by-step guidance designed for safe experimentation and future production use.
Whether you want to automate routine configuration, implement AI-driven troubleshooting, or build compliance monitoring systems, this cookbook helps you bridge the gap between your networking expertise and modern AI capabilities.What you will learn
Understand AI LLM landscape and key parameters for networking tasks
Create OpenAI-enabled scripts for daily network engineering workflows
Master prompt engineering techniques for improved AI outputs
Build local LLM using Ollama for network applications
Chain language models with LangChain for complex network solutions
Develop AI application frontends using Streamlit framework
Design robust backends for network AI applications
Build end-to-end network copilot with all learned techniques
Who this book is forAI for Networking Cookbook is for experienced network engineers, network architects, and DevOps professionals who want to leverage AI and LLM technologies to enhance their network automation capabilities. Networking professionals looking to integrate conversational AI development, prompt engineering, and modern AI tools like OpenAI APIs, LangChain, and local LLM models into their workflows will find this book invaluable.
Basic familiarity with networking concepts, configurations, Python would be helpful. No prior AI or advanced programming experience is required.
Key Features
Leverage AI assistants like OpenAI and Claud to build network automation solutions
Use prompt engineering and AI tools to automate network setup, monitoring, and threat detection
Build AI-assisted network configuration, monitoring, and management with multi-vendor APIs
Book DescriptionTransform your approach to network automation with the power of AI LLM assistants. This cookbook gives you practical, hands-on recipes for rapidly and efficiently building custom automation solutions with AI.
You'll discover how to apply tools such as Vibe coding for conversational development, OpenAI API scripts, prompt engineering for better outputs, fine-tuning local LLMs, combining models with LangChain, and creating rapid frontends with Streamlit.
Across all the chapters, you'll progress from simple Python scripts to advanced AI-assisted automation techniques, including multi-vendor API integration. Along the way, you'll learn how to harness AI for network configuration, monitoring, security, and troubleshooting.
Each recipe includes realistic mock data, complete code examples, and step-by-step guidance designed for safe experimentation and future production use.
Whether you want to automate routine configuration, implement AI-driven troubleshooting, or build compliance monitoring systems, this cookbook helps you bridge the gap between your networking expertise and modern AI capabilities.What you will learn
Understand AI LLM landscape and key parameters for networking tasks
Create OpenAI-enabled scripts for daily network engineering workflows
Master prompt engineering techniques for improved AI outputs
Build local LLM using Ollama for network applications
Chain language models with LangChain for complex network solutions
Develop AI application frontends using Streamlit framework
Design robust backends for network AI applications
Build end-to-end network copilot with all learned techniques
Who this book is forAI for Networking Cookbook is for experienced network engineers, network architects, and DevOps professionals who want to leverage AI and LLM technologies to enhance their network automation capabilities. Networking professionals looking to integrate conversational AI development, prompt engineering, and modern AI tools like OpenAI APIs, LangChain, and local LLM models into their workflows will find this book invaluable.
Basic familiarity with networking concepts, configurations, Python would be helpful. No prior AI or advanced programming experience is required.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
647 gr
ISBN-13
978-1-80580-799-5 (9781805807995)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Eric Chou is a seasoned technologist with over 20 years of experience. He has worked on some of the largest networks in the industry while working at Amazon, Azure, and other Fortune 500 companies. Eric is passionate about network automation, Python, DevOps, and helping companies build better security postures. In addition to being the author of Mastering Python Networking (Packt), he is the author or co-author of other top-selling books and highly-rated online classes. Eric is the primary inventor or co-inventor for three U.S. patents in IP telephony and networking. He shares his deep interest in technology through his books, classes, blog, and contributes to some of the popular Python open source projects.
Content
Table of Contents
The AI LLM Landscape and Key Parameters
OpenAI API Recipes for Network Engineers
Prompt Engineering for Reliable Outputs
Local LLM Model Playgrounds in Network Engineering
Chaining Language Models for Network Tasks
Building AI LLM Network Application Frontend with Streamlit
Building AI LLM Application Backends for Network Applications
Building Network Co-Pilot
Network Monitoring and Performance Use Cases
Network Security Use Cases
The AI LLM Landscape and Key Parameters
OpenAI API Recipes for Network Engineers
Prompt Engineering for Reliable Outputs
Local LLM Model Playgrounds in Network Engineering
Chaining Language Models for Network Tasks
Building AI LLM Network Application Frontend with Streamlit
Building AI LLM Application Backends for Network Applications
Building Network Co-Pilot
Network Monitoring and Performance Use Cases
Network Security Use Cases