AI Networking Cookbook : Practical Recipes for AI-Assisted Network Automation and Development.

Format:

Book

Author/Creator:

Chou, Eric.

Language:

English

Subjects (All):

Computer networks--Automation.

Computer networks.

Physical Description:

1 online resource (347 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2026.

Summary:

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.

Contents:

Cover

FM

Copyright

Foreword

Contributors

Table of Contents

Preface

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Chapter 1: The AI LLM Landscape and Key Parameters

Technical requirements

1.1 Setting up an OpenAI environment

Getting ready

How to do it…

How it works…

There's more…

1.2 Experiment with OpenAI LLM models and model parameters

1.3 Experimenting with OpenAI System message

1.4 Experimenting with OpenAI prompt engineering in chat

1.5 Python scripts with OpenAI

1.6 Local AI model Ollama

1.7 Python scripts with Ollama

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Summary

Chapter 2: OpenAI Recipes for Network Engineers

2.1 Using curl for OpenAI API communication

2.2 Using Postman with the OpenAI API

2.3 Fine-tuning OpenAI responses

2.4 Generating network topology with OpenAI

2.5 Generating methods of procedure (MOPs) with OpenAI

Chapter 3: Prompt Engineering for Reliable Outputs

Technical requirements.

3.1 Giving directions to AI models

See also

3.2 Specifying return formats

3.3 Giving examples

3.4 Providing feedback

3.5 Building prompts gradually

The network engineer's advantage

Chapter 4: Local AI LLM Playground in Network Engineering

4.1 Downloading Code Ollama models

4.2 Fine-tuning LLM resources

4.3 Generating network code examples with Code Llama

4.4 Using the Llama 2 Uncensored model for advanced code generation

4.5 Tweaking models with custom network data

4.6 Using a local LLM for network documentation generation

Chapter 5: LangChain for Networking Tasks

What is LangChain all about?

5.1 Installing and setting up LangChain

5.2 Creating a network configuration analyzer

5.3 Using prompt templates for reusability.

Getting ready

5.4 Combining models with simple chains

5.5 Using agents with LangChain

Chapter 6: Building an AI LLM Network Application Frontend with Streamlit

6.1 Installing and setting up Streamlit

6.2 Creating your first visualization

6.3 Creating input fields

6.4 Creating a navigation menu

6.5 Organizing projects with folders

Chapter 7: Building AI LLM Application Backends

7.1 Simple network AI APIs

7.2 Adding device context

7.3 Adding database storage

7.4 Simple web interface

7.5 Docker deployment

Chapter 8: Building a Network Co-Pilot

8.1 Model selection and evaluation

8.2 Building the core AI engine

8.3 Network knowledge integration

Chapter 9: Network Monitoring and Performance Use Cases with MCP

MCP overview

MCP concepts

9.1 AI-powered network health analysis

9.2 Predictive network performance analysis

9.3 Intelligent network optimization and auto-remediation

Chapter 10: Network Security through Vibe Coding

10.1 Setting up GitHub Copilot

10.2 Setting up Claude Code

10.3 Conversational log analysis and threat detection

10.4 Rapid security script development for incident response

Chapter 11: Unlock Your Exclusive Benefits

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Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-80580-798-6

9781805807988

OCLC:

1559963372

Publisher Number:

CIPO000315618