Artificial intelligence is changing businesses around the world. According to the CEO of Salesforce, AI is already doing 50% of the work at the company. But perhaps one field where the impact of AI is the most tangible is the IT industry.  

Despite the anxiety around the future of software developers with AI entering the field, as developers, we’re always looking for ways to become more efficient, reduce the time we spend on repetitive or boilerplate tasks and deliver faster. And AI is exactly helping us do that.  

AI will not replace developers as we and many experts see it; rather, it will augment our work with its advanced capabilities. AI development services and tools can assist in every aspect of software development to make a developer’s life easier. 

So, to show with an example, we focused on integrating AI tools into our daily development workflow. In this article, we’ll walk you through exactly how we achieved that, what tools we used, and how you can replicate this in your projects. 

The problem: Time-consuming development processes 

We all know the software developer stereotypes. That we’re nerdy, we chug down dozens of cups of coffee while being glued to our computers writing thousands of lines of code. 

Yes, we do love coffee and spend most of our time in front of computer screens, but we aren’t anti-social geeks. While there is nothing wrong in being an introvert, developers are usually excellent communicators because software development isn’t just about coding. It’s a vibrant field that requires creativity and collaboration. 

However, it is also a very technical line of work that can be exhausting, both physically and mentally. A software developer has to face many challenges to deliver the software that powers our modern world. 

Before AI software development, quite a lot of a developer’s time was consumed by the drudgery of doing dull, menial work that soaked up hours of productive potential.  

Here are a few of those bottlenecks that we personally experienced in our workflow: 

Writing repetitive boilerplate code 

 Boilerplate code are those repetitive lines of programming that every developer knows by heart. Such parts of the code are extensively used across the project with little to no variation. For example, login functions, form validations, and basic CRUD operations. These fragments are necessary to make the application work but offer little creative challenge. They’re also easy to mess up and introduce tiny bugs that can take SQA services hours to track down. 

Writing boilerplate code is a very monotonous process that can lead to developer fatigue and wastage of time. It also distracts programmers from high-value business logic that the software is intended to solve.  

From a technical perspective, boilerplate code also makes the codebase larger which is harder to navigate and maintain.  

Creating unit tests manually 

Unit testing is another tedious process that can be a real slog in software development. Developers write unit tests to individually verify units of code within a program. It is also known as module testing and is an integral part of making excellent software. 

It is an important part of software quality assurance, but writing these tests is very time-consuming and complex. Maintaining unit tests is even more difficult than creating them. With each upgrade or change, tests need to be updated to reflect changes. So, if a test is poorly written, it can break with even minor code changes. 

Without unit tests, bugs can slip through. But spend too much time on them, and deadlines slip instead. Given how closely unit testing ties into broader software testing services, it’s clear that even small inefficiencies here can ripple through the entire QA process.

Digging through documentation to understand unfamiliar APIs 

APIs are to software development what nerves are to the human body. They are the communication pathways that let software send signals, respond to input, and operate in full synchronization.  

But APIs are very intricate and require thorough documentation for developers to understand and use them effectively. If the API documentation is vague, outdated, or lacks practical examples, developers have to spend extra time deciphering how to use and integrate them. 

Moreover, different APIs follow different documentation standards. For example, a microservices API will have a different standard than APIs for web application development. This makes it difficult for developers to quickly grasp new interfaces.  

Even with well-structured documentation, the learning curve can be really steep, and developers have to invest significant time to understand underlying concepts and data models before they can even begin coding. 

Reviewing code and performing manual refactoring 

Finally, code review and refactoring are essential but slow SQA processes. Code review is critical to ensure code quality and catch bugs, while refactoring is done to make the code more legible and easier to maintain. 

One of the reasons why code review is so time-consuming is because developers often have review large amounts of code all at once. This manual scrutiny of every line of code can be very taxing. With refactoring, there is also the risk of introducing bugs because you aren’t only reviewing but restructuring the existing code without changing its behaviour.  

In short, we realized we needed to optimize these non-creative, routine processes. Each of these tasks consumed valuable time that could have been better spent solving core business problems. 

The Turning Point: Introducing AI to the Stack

The use of AI in software development has really taken off after the release of generative AI platforms like ChatGPT. Since then, several custom AI solutions and agents have emerged to tackle every aspect of software development. 

We initially used AI for generating code snippets, but the real game-changer was when we began incorporating AI tools across all stages of the software development lifecycle (SDLC): 

Code completion and snippets with GitHub Copilot 

GitHub Copilot is an AI-powered coding assistant. It uses advanced AI solutions to offer real-time suggestions and automate repetitive coding tasks. GitHub Copilot became our pair programmer. Instead of writing every line of code from scratch, we started leveraging Copilot to autocomplete functions, which helps in creating REST APIs, generating regex patterns, or suggesting data structures. This dramatically reduced the time we spent on syntax-level tasks. 

Beyond just code, Copilot also suggested relevant comments to us. This really helped us get rid of boilerplate code significantly. 

The more we interacted with Copilot, the more it learned from our coding style and preferences, which allowed it to provide increasingly personalized and accurate suggestions over time.  

Time saved: ~20% on average during feature development. 

Documentation and onboarding with ChatGPT 

When working with unfamiliar APIs or diving into legacy code, ChatGPT helped us understand concepts quickly. We would paste code blocks and ask for explanations or usage examples. 

ChatGPT also generated various forms of documentation, including API documentation and code comments. We also used it as a 24/7 virtual assistant to explain complex codebases and FAQs about our project setup. 

Additionally, the LLM is very useful to improve code consistency. Just give it your code and it helps in enforcing consistency in documentation style and terminology, which leads to high-quality and more readable documents. 

Time saved: ~30-40% when exploring new codebases or third-party tools. 

Test case generation 

Using AI, we auto-generated unit and integration test cases by simply providing the function signature and describing the desired behavior. This saved hours, especially in test-driven development (TDD). 

AI also provided us with edge cases and potential vulnerabilities that were hard to detect by human testers. Moreover, it also gave us a faster feedback loop with developers quickly identifying and fixing bugs. 

We also explored predictive analytics AI tools to identify high-priority test cases or areas of the app that were more prone to defects. Finally, custom AI solutions also allowed us to adapt and generate test cases that were relevant to constant manual updates. 

Time saved: Up to 50% for test writing. 

Automated code review and refactoring 

Instead of going through each line manually, we used AI to scan and suggest improvements. We didn’t adopt these changes blindly but used them as a second opinion to speed up refactoring. 

But AI could review pull requests in seconds, which would’ve taken us hours. It immediately pointed out security risks, performance issues, and code style inconsistencies with complete objectivity.  

Offloading code review and refactoring to AI took much burden off of our backs. It not only allowed us to focus on higher-level concerns but actually helped us get rid of physical fatigue.  

Time saved: ~15-25% during code review phases. 

Prompt-based feature prototyping 

Generative AI significantly accelerates the initial stages of SDLC by turning ideas into tangible prototypes. Sometimes, we just described the feature we wanted in plain English to an AI model and received functional boilerplate or React component code as a starting point. This was a huge leap in speed.  

This allowed us to experiment with different design choices at an unprecedented pace. Moreover, using prompt-based AI tools, we also involved our non-technical staff like product managers and designers in the ideation process. 

Time saved: ~25% for early-stage prototyping. 

Real-world application: A case study 

In one of our internal tools built in React and Node.js, we had to ship a feature that filtered and grouped user transactions based on multiple attributes. Normally, this would have taken 3-4 days including testing and UI scaffolding. 

With the help of Copilot and ChatGPT: 

• The initial backend filtering logic was written in under 1 hour. 
• Frontend filtering UI was scaffolded using AI suggestions. 
• All unit tests were generated automatically. 
• Code review time was cut in half. 

Total time: ~1.5 days vs 3-4 days previously. 

Lessons learned and best practices 

The 40% reduction in development time only happened after integrating AI thoughtfully. Our experience gave us working knowledge of where AI shines and where it still needs a human touch. 

Based on that experience, here are some of the best practices that we recommend for using AI in your software development: 

AI is a co-pilot, not an autopilot 

Don’t blindly copy-paste AI-generated code. Treat AI as a productivity partner, not a replacement for thinking. Developers who expect AI to handle everything end up creating more mess than cleaning it.  

Remember that AI tools are smart, but not infallible. They hallucinate and don’t always do the right thing. Use them to augment your work as the ultimate responsibility of a project remains with the human developer. 

Customize prompts for better results 

A well-structured prompt gets better output. So, be specific about what you want. AI understands the problem better if you provide sufficient context in the prompt.  

For example, mention the functions, classes, or architectural patterns that you want explicitly so that the AI generates code that integrates smoothly with your project.  

We personally found it helpful to instruct the AI to act as a specific persona, such as a senior Python developer, to get the output that aligned with our needs. 

Use version control religiously 

AI-generated code can be experimental. Additionally, AI tools generate a significant amount of code, which becomes difficult to track what parts of the codebase were AI-generated or human-written. Therefore, make a habit of using version control systems for managing changes and to ensure the reproducibility of AI-assisted code. 

Version control also provides an auditable trail of all code changes, which is essential in regulated industries with strict compliance requirements like HealthTech. 

Combine tools 

We didn’t rely on just one AI tool. The synergy between Copilot, ChatGPT, and testing tools amplified the time savings. No single AI tool can address all the aspects of the development process. Therefore, combine their power to create a powerful and efficient AI-based development environment. 

Conclusion 

AI has a lot to offer to both junior and senior developers that use it properly. Junior developers can skip a lot of the mundane training process with automation and learn the big-picture architecture. It will increase expectations from them but will greatly enhance their career growth. 

Senior developers can use AI to focus more time designing systems and solving problems that relate to core business objectives. If you’re a developer looking to level up in 2025, we highly recommend exploring how AI can assist you. Start small, experiment, and measure your gains. You might just find yourself getting more done in less time and enjoying coding even more. 

Integrating AI into our workflow has been one of the most impactful decisions we’ve made. Not only did it saved time, but we also reduced burnout and had more mental bandwidth to focus on design, architecture, and collaboration. 

Let AI handle the tedious tasks. And you can focus on the creative problem-solving that only humans can do, so far. 

Not sure how to begin with? Xavor can assist you implement modern AI solutions in your workflow regardless of any industry. Our AI and machine learning services work with the latest AI innovations in our offshore centres of excellence in U.S., China, and Pakistan.  

Contact us at [email protected] to start your AI journey with us.