Copilot in Action: From Idea to Production Code

This is Part 3 of a 4-part series on building Andon-SSP with GitHub Copilot. Read Part 1 and Part 2 for context.

Coming soon: This post will showcase real-world examples of GitHub Copilot generating production code for Andon-SSP. You'll see actual prompts, generated code, and the refinement process.

What This Post Will Cover

• Generating Dataverse entities and service interfaces: From comment to production-ready code in 15 minutes
• Building SignalR hub infrastructure: Complex connection management, group subscriptions, error recovery
• Creating the Windows Gateway: RF protocol translation for manufacturing devices
• Writing comprehensive unit tests: From 40% to 85%+ coverage with AI assistance
• The iteration loop: Prompt → Generate → Refine → Ship

Example 1: Entity Generation with Validation

Before Copilot (traditional approach):

• Manually write entity class: 30 minutes
• Add validation logic: 15 minutes
• Create repository interface: 20 minutes
• Implement CRUD operations: 45 minutes
• Total: ~2 hours per entity

With Copilot:

• Write descriptive comment
• Copilot generates entity with validation
• Review and refine
• Total: ~15 minutes per entity

I'll show the exact prompt and generated code in this post.

Example 2: SignalR Hub for Production Alerts

Real-time production alerts require complex SignalR logic: connection management, group subscriptions, error recovery, reconnection strategies.

The Challenge:

• Clients must join groups by production cell ID
• Broadcast status updates to specific cells only
• Handle reconnections gracefully
• Maintain client state across disconnects
• Log all events for debugging

The Copilot-Assisted Solution:

I'll share the multi-part prompting strategy that generated a production-ready SignalR hub with proper async/await patterns, connection lifecycle management, and structured logging.

Example 3: The Windows Gateway Service

One of the most complex pieces: a Windows service that translates RF device protocols into HTTP requests to the cloud API.

Requirements:

• Run as Windows service
• Monitor COM ports for RF device messages
• Parse proprietary protocol formats
• Queue messages for retry on network failure
• Handle graceful shutdown

Copilot helped generate the service skeleton, COM port monitoring logic, and retry mechanisms. I'll show how iterative prompting refined the solution.

Example 4: Test Coverage Explosion

The Pattern:

1. Write production code
2. Prompt: "Generate xUnit tests covering success, failure, and edge cases"
3. Review generated tests
4. Add domain-specific test cases manually
5. Result: 85%+ coverage vs typical 40%

I'll share examples of Copilot-generated test suites and what I added manually.

Part 4 (final): Lessons learned—what works brilliantly with AI pair programming, what requires human oversight, development velocity metrics, and best practices for AI-assisted enterprise development.

Questions about using Copilot for complex enterprise scenarios? Contact me—I've documented hundreds of examples.