In the rapidly evolving world of technology, artificial intelligence (AI) has sparked intense debates. Some predict that AI will replace software engineers entirely, while others argue that building products has become so straightforward that traditional software engineering skills are obsolete. I strongly disagree with both views. In fact, learning software engineering is more valuable than ever. With a solid foundation, you can leverage AI to handle edge cases, make informed decisions, and automate boilerplate code. This turns you into a productivity powerhouse. In this article, I’ll share how I used AI to build a complex product in just three weeks (part-time), what I learned from the experience, and why skilled engineers are uniquely positioned to thrive in this AI-driven era.
The Power of Experience in an AI World
Before diving into the specifics, consider this: I’m currently working on another project where I reached a minimum viable product (MVP) in just a couple of days. Why so fast? My years of software engineering experience allowed me to architect the solution efficiently, anticipate pitfalls, and integrate AI tools seamlessly. Without that knowledge, AI alone wouldn’t have sufficed. It’s the synergy between human expertise and AI that creates magic.
This brings me to the main story: I was tasked with building a trading academy API. This wasn’t a simple app; it needed a full learning management system (LMS) for courses, channels for trading signals, direct messaging (DMs) for user interactions, and subscription management. A project of this scope would typically take several months for a solo developer. But by combining my engineering skills with AI assistance, I completed it in three weeks. And it was not even full-time. If I’d dedicated myself entirely, it could have been done in two. Here’s how I did it.
Laying the Foundation: Structure and Tech Choices
Success started with thoughtful planning, drawing on my software engineering background. I knew the pros and cons of common architectures; for instance, the Model-View-Controller (MVC) pattern can lead to cluttered code in larger projects. Instead, I opted for a feature-based structure to keep things modular and scalable.
I chose Node.js with Express.js for the backend to enable rapid development, paired with MongoDB for flexible data storage. Crucially, I designed the features to be loosely coupled, allowing me to build and test one at a time without ripple effects. This modularity is a hallmark of good engineering and made AI integration smoother.
For each feature, I enforced a consistent structure: controllers for handling requests, services for business logic, validators for input checks, routes for API endpoints, models for data schemas, types for TypeScript definitions, and middlewares for cross-cutting concerns. I also created a shared utilities folder for reusable components.
Building Features with AI: From Auth to Messaging
I kicked off with authentication, a standard but critical component. Using Claude (an AI coding assistant), I generated boilerplate code for email-based auth with refresh tokens. The output was well-structured, but it had a few errors, like outdated dependencies, which I quickly fixed using my knowledge. After testing in Postman, it was solid.
Next came user management. I separated the User model (focused on authentication) from the Profile model (for easily updatable user details). Again, Claude provided the CRUD (Create, Read, Update, Delete) boilerplate. I didn’t accept it blindly; I reviewed for adherence to my structure, fixed issues, and ensured everything worked through rigorous testing.
File management followed suit. Claude generated the code, but I spotted an outdated AWS SDK version in the output. This was a reminder that AI tools might not always reflect the latest updates. Drawing on my experience, I upgraded it, set up an AWS S3 bucket with a CDN for efficient delivery, and verified functionality.
Courses were straightforward: same process, with minor error fixes leading to a working LMS module.
Messaging proved trickier. Midway through, I hit Claude’s chat limit. Undeterred, I had the AI generate a detailed prompt summarizing the project’s state, structure, and progress. I pasted this into a new chat session, allowing the AI to pick up seamlessly. The structure shifted slightly at first, but I guided it back on track. In the end, I handled most error resolutions myself, making necessary updates to ensure robust DMs and channels.
Lessons Learned: AI as a Bootstrapper, Not a Replacement
This project reinforced a key insight: AI excels at bootstrapping. It generates standard solutions quickly, handles repetitive tasks, and accelerates development. But it’s not infallible. Errors, outdated info, and context loss require human intervention. That’s where software engineering shines: knowing what to ask, spotting flaws, and refining outputs.
AI has empowered engineers to become solo founders, pushing products to market at unprecedented speeds. It hasn’t made us obsolete; it’s elevated us to „god-like“ productivity levels. Imagine completing months of work in weeks. That’s the reality for those who know how to wield it. A novice „vibe coder“ couldn’t achieve this; only a skilled engineer can direct AI effectively, avoiding pitfalls and ensuring quality. In my case, AI shaved off boilerplate drudgery, letting me focus on architecture and innovation. The result? A fully functional trading academy API, delivered efficiently.
Conclusion: Embrace AI, But Build Your Foundation First
AI isn’t here to replace software engineers. It’s here to amplify them. By mastering the fundamentals, you can become a 10x engineer: faster, more innovative, and capable of solo feats that once required teams. My experience proves it: leverage AI for speed, but rely on your expertise for precision.For inquiries, reach out via X at @janvinsha.