Using ChatGPT to Write Network Documentation

Network documentation is one of those tasks we all know we should do better, but it often gets pushed aside for "more urgent" work. What if I told you that ChatGPT can help you create professional network documentation in minutes instead of hours?

Let's explore how to use ChatGPT effectively for common network documentation tasks, with specific prompts you can use right away.

Network Diagram Descriptions

Visual network diagrams are great, but they need accompanying text descriptions for completeness. Here's a prompt that works well:

Create a detailed description of a network topology with the following components:
- Core switch: Cisco Catalyst 9500 (10.1.1.1)
- Two distribution switches: Cisco Catalyst 3850 (10.1.1.2, 10.1.1.3)
- Four access switches: Cisco Catalyst 2960 series
- Firewall: Palo Alto PA-3220 (10.1.1.254)
- Internet connection via ISP router

Include IP addressing scheme, VLAN information, and redundancy details. Format as professional documentation.

The key here is being specific about your equipment and including actual IP addresses. ChatGPT will generate a structured description that includes logical flow, redundancy paths, and technical details.

Change Request Templates

Change requests need to be thorough but readable. Try this prompt:

Create a network change request template for upgrading switch firmware. Include:
- Pre-change checklist
- Step-by-step implementation procedure
- Rollback plan
- Risk assessment
- Maintenance window requirements
- Testing verification steps

Make it suitable for a medium-sized enterprise network.

ChatGPT excels at creating comprehensive templates because it follows logical structures. You'll get sections for business justification, technical details, and approval workflows that you can customize for your organization.

Runbook Procedures

Runbooks need to be clear enough for anyone on your team to follow. Here's an effective approach:

Write a network troubleshooting runbook for "Users Cannot Access Internet." 
Include:
- Initial diagnosis steps
- Common causes and solutions
- CLI commands for Cisco equipment
- Escalation criteria
- Contact information placeholders

Target audience: Level 1 network support technicians.

The magic happens when you specify the target audience. ChatGPT will adjust the technical depth and include more detailed explanations for less experienced staff.

Incident Reports

Post-incident documentation is crucial but time-sensitive. Use this template prompt:

Create an incident report template for network outages including:
- Executive summary (2-3 sentences)
- Timeline of events
- Root cause analysis
- Services affected
- Resolution steps taken
- Lessons learned
- Action items for prevention

Format for senior management review.

Pro Tips for Better Prompting

Be Specific: Instead of "write documentation for a network," specify "write a troubleshooting guide for OSPF neighbor adjacency issues on Cisco routers."

Define Your Audience: Always mention who will read this documentation. A guide for senior engineers looks very different from one for help desk staff.

Request Structure: Ask for specific sections, bullet points, or numbered steps. ChatGPT responds well to clear formatting requests.

Include Context: Mention your environment (Cisco, Juniper, cloud-based, etc.) and scale (small office, enterprise, service provider).

Refining the Output

Don't accept the first response. Follow up with refinement prompts like:

• "Make this more technical and include specific CLI commands"
• "Simplify this for junior technicians"
• "Add a troubleshooting flowchart in text format"
• "Include common error messages and their meanings"

Remember, ChatGPT is a starting point, not the finish line. Always review the output for accuracy, add your organization-specific details, and validate any technical procedures before putting them into production use.

What's Next

Now that you can generate solid documentation quickly, the next step is integrating ChatGPT into your network monitoring and alerting workflows. We'll explore how to use AI to interpret log files and suggest automated responses to common network issues.