Transform your network operations with AI-powered automation, and learn code generation, prompt engineering, and practical recipes for building custom network tools using AI assistants and Python

Key Features

• Leverage AI assistants like OpenAI and Claude 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 workflows with multi-vendor APIs
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Transform your approach to network automation with the power of AI LLM assistants guided by hands-on recipes for building custom automation solutions quickly using artificial intelligence.

You’ll learn tools and techniques such as Vibe coding for conversational development, OpenAI API scripts, prompt engineering for better outputs, local LLM fine-tuning, combining models with LangChain, and Streamlit-based frontends development. The book progresses from simple Python scripts to advanced AI-assisted automation techniques, including multi-vendor API integration, showing you how AI can enhance network configuration, monitoring, security, and troubleshooting.

Each recipe presents realistic mock data, complete code examples, and step-by-step guidance, creating a safe environment for experimentation while building a solid foundation for future production use. Whether you want to automate routine configuration, implement AI-driven troubleshooting, or build compliance monitoring systems, this cookbook helps you connect your networking expertise with the capabilities of modern AI.

What you will learn

• Understand the 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 LLMs using Ollama for network applications
• Chain language models with LangChain for complex network solutions
• Develop AI application frontends using the Streamlit framework
• Design robust backends for network AI applications
• Build an end-to-end network copilot by integrating all the techniques you’ve learned

Who this book is for

The AI for Networking Cookbook is for experienced network engineers, network architects, and DevOps professionals who want to enhance their network automation capabilities using AI and LLM technologies. It is especially invaluable for 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.

Familiarity with basic networking concepts, configurations, and Python is helpful, but no prior AI or advanced programming experience is required.

Table of Contents

1. The AI LLM Landscape and Key Parameters
2. OpenAI Recipes for Network Engineers
3. Prompt Engineering for Reliable Outputs
4. Local AI LLM Playground in Network Engineering
5. LangChain for Networking Tasks
6. Building an AI LLM Network Application Frontend with Streamlit
7. Building AI LLM Application Backends
8. Building a Network Co-Pilot
9. Network Monitoring and Performance Use Cases with MCP
10. Network Security through Vibe Coding