Coding with Copilot: How AI is accelerating our development process

Writing code has always been a deeply personal activity for me, blending logic, creativity, and experience into unique solutions. That process has always been one of the most rewarding aspects of development. So, the idea of asking an AI to generate code snippets felt, at first, like it might dilute that experience. Would I be giving up too much of the enjoyment? Would I become more of a prompt engineer than a developer? I’ll admit, I was reluctant to use an AI coding assistant. I wanted to retain full control, to stay close to the code. But curiosity - and a desire to stay ahead - eventually won out. 

One of the most exciting tools I’ve adopted recently is GitHub Copilot, an AI-powered coding assistant that’s been quietly transforming the way we build software. I’ve been using it extensively in a Laravel project, and the experience has been nothing short of game-changing. 

Laravel, a modern PHP framework known for its elegant syntax and developer-friendly architecture, has always been a pleasure to work with. But pairing it with Copilot has taken productivity and code quality to a new level. 

A true pair-programmer 

Copilot doesn’t just autocomplete code, it understands context. Whether I was scaffolding classes, writing unit tests and documenting code, Copilot offered intelligent suggestions that often felt like having another developer sitting beside me, ready to help at every turn. The result? I produced significantly more code in less time, and improved quality through not needing to worry about comment blocks or style. 

But let’s be clear: this isn’t “vibe coding.”, you still need to design the application and understand the code being created otherwise you’ll find yourself with a complex application consisting of code soup that’s not maintainable. Copilot is a powerful assistant, not a replacement for expertise or attention to detail.  

Whilst I was in the flow of writing the code, Copilot assisted with the tedious tasks such as documentation and code formatting. My experience showed the more consistent I was with type hinting the better Copilot performed, offering refactoring opportunities earlier in the SDLC. As it became more familiar with my style and approach to design patterns, the auto-complete suggestions were usually on the money, but it wasn’t infallible. It had a tendency to guess URLs, variable names, and namespaces as part of the integration I was working on, and if you aren’t paying attention that can cause problems. This led me to see how the tool is most effective when paired with a comprehensive suite of tests and rigorous peer reviews, both of which are standard practice in our development process but add that additional level of verification and quality. 

Speed meets precision 

The most immediate benefit I noticed was the acceleration of my development process. Tasks that would typically take hours, like setting up boilerplate, writing repetitive logic, or generating test cases are now completed in minutes. This freed me up to focus on the more complex, creative aspects of problem-solving. 

It also improves consistency. Copilot helps enforce coding standards and documentation practices, reducing the cognitive load and making our codebase easier to maintain and scale. 

What this means for our clients 

For our clients, this translates into faster delivery and more robust solutions. By integrating AI into our development workflow we’re able to supercharge our team’s expertise. It’s a reflection of our commitment to innovation, efficiency, and excellence. 

Whether we’re building custom platforms, integrating APIs, or developing internal tools, our use of AI would ensure we’re delivering solutions that are not only technically sound but also future-ready and maintainable. 

Responsible innovation 

Of course, adopting AI responsibly is key. We’re mindful of the limitations - as I saw, Copilot can occasionally suggest incorrect or potentially insecure code, and it’s not a substitute for human judgement. That’s why we maintain strict code review protocols and ensure every line of code is reviewed by experienced developers. 

We also take data privacy and security seriously. Copilot doesn’t access proprietary client data, and our use of AI tools is always aligned with ISO 27001. 

Looking ahead 

This is just the beginning and anything which makes our lives easier is continuous evaluation. We’re already exploring how AI can support other areas of our work, from automated testing and deployment pipelines to content generation and project estimation. The goal is simple: to build smarter, faster, and better. 

Overall, my experience with GitHub Copilot to date has been broadly positive. The concerns I had about losing the enjoyment of coding have proven unfounded and if anything, the tool has enhanced the experience. It’s helped me produce more, think faster, and stay focused on the parts of development I enjoy most. I’ll definitely be continuing to use it, and I’m excited to see how it evolves. 

What’s next for AI in development? 

While tools like GitHub Copilot are already making a tangible impact, we’re only scratching the surface of what AI can do in software development. In future, we expect to see AI assistants evolve from reactive helpers to proactive collaborators, capable of understanding entire codebases, suggesting architectural improvements, and even identifying potential bugs before they’re written. 

Connecting MCPs with natural language-to-code generation, will allow developers to describe functionality in plain English and have AI generate fully functional modules. Combined with automated testing and deployment, this could dramatically reduce time-to-market for digital products. 

As these capabilities mature, our focus will remain on using them responsibly: to enhance developer creativity, not replace it; to improve quality, not cut corners; and to deliver better outcomes for our clients.