AI isn't just hype anymore. It's actually changing how I write, test, and ship code every day. Over the past 2.5 years, I've been using various AI tools in my work, and honestly, the results have surprised me. Right now feels like a turning point for developers. Tools like GitHub Copilot with GPT and Claude are tools I rely on daily to get work done faster and better. There are some amazing benefits, but also a few things to watch out for.

Beyond Boilerplate: Where AI Really Shines

When AI coding assistants first emerged, they were primarily useful for generating boilerplate code and providing autocomplete suggestions. I used to ask it regarding a specific coding question and it used to give a response which I would then stitch into my codebase. But the landscape has evolved dramatically. Today, I just prompt it with the bug description in agentic mode and it fixes the issue for me.

1. Complex Bug Detection and Resolution

One of the most impressive capabilities I've experienced is AI's ability to pinpoint bugs in large, complex codebases. Recently, when assigned a JIRA task about a user reported bug in our large WPF/WCF solution consisting of 20+ projects and thousands of files, I simply prompted GitHub Copilot with the bug description.

The result was remarkable: Copilot not only identified the exact file and line of code responsible for the issue but also provided the correct fix. This level of contextual understanding across massive codebases represents a fundamental shift in how we can approach debugging.

Limitations: However, I've noticed some challenges when working with feature addition stories in our corporate environment. Using GPT 5 preview (P.S. only GPT models available in our corporate setup) often results in "token limit exceeded" errors when trying to make changes to files, especially in large codebases. GPT 4, while better at handling these limits, struggles with direct file editing. It excels at generating code suggestions, but developers still need to manually copy and paste the recommendations.

2. Performance Optimization Through Code Review

AI has become invaluable for code reviews on GitHub pull requests. A recent example that stands out: Copilot helped me reduce time complexity from 1,000,000 maximum operations down to just 2,000 operations. This was a dramatic improvement that would have been easy to miss in manual review!

The specific optimization: It suggested replacing .FirstOrDefault() inside a loop with a Dictionary and TryGetValue, reducing complexity from O(n²) to O(n) + O(1).

Based on these results, I now actively encourage fellow developers to integrate Copilot into their code review processes. The combination of AI assisted review alongside traditional manual review has proven to catch both logical errors and performance optimizations that either method might miss alone.

My Journey with AI So Far

• Copilot on the web
• Git commit message on Visual Studio
• XUnit tests Generation
• "Ask" mode in GitHub Copilot
• Agentic mode in GitHub Copilot (More recently, letting AI take more autonomous actions in my workflow)

Tools I Use Personally and at Work (software development context)

• GitHub Copilot: Primary tool for bug fixing, writing x unit tests, code refactoring/optimization in Visual Studio
• Copilot for Pull Requests: For GitHub code review
• Copilot with Claude: I use it in VSCode(personal) and it also helped me build this site.

Looking Forward

I'm looking forward to seeing how AI continues to evolve and integrate into my workflow. I'd like to see it take on more complex tasks, feature additions on huge code bases and just adding a url to a well refined JIRA ticket should be enough for it to perform the task. And developer would just have to review and refine.

Going a step further, it should make sure the code meets all security checks like SonarQube, VeraCode, CheckMarx.