How I Use AI to Build Software 10x Faster (Without Replacing My Engineers)

AI is everywhere in tech right now—and depending on who you ask, it’s either the future of development or the end of engineering as we know it.

Here’s my take: AI won’t replace great engineers. But it can make them 10x more effective.

I run a full-stack dev company and coding bootcamp. We build fast, we build lean, and we build with AI—without losing control of quality or creativity. In this post, I’ll show you exactly how we use AI to speed up software development in real-world projects, without sacrificing quality—or people.

Let’s break it down.

1. We Use AI to Write Code (But Not Like You Think)

Yes, we use AI to write code. No, we don’t trust it blindly.

Tools like ChatGPT and GitHub Copilot are powerful, but they don’t understand your product. They just predict patterns. That’s why we treat AI like an intern—it can help with boilerplate and repetitive tasks, but every line it writes is reviewed by a senior dev.

For example, we might ask GPT to scaffold a React component or generate test cases. But before anything gets merged, we QA it manually. Why? Because AI doesn’t know your architecture, business logic, or performance requirements.

One time, AI wrote dashboard code that looked great—but re-rendered everything on every click. It killed performance. If we hadn’t reviewed it, it would’ve been a UX disaster.

Key takeaway: AI is an assistant, not an engineer. Use it to accelerate, not automate blindly.

2. We Scope Projects Faster (And Smarter)

AI isn’t just for code. It’s a killer tool for scoping projects—if you use it right.

Before, our scoping process took days: long documents, endless back-and-forth, and lots of edge case guesswork. Now, we jot down rough bullets in Notion—tech stack, features, auth methods, DB setup—and drop it into GPT-4.

Then we ask: “What are we missing?”

The AI highlights potential oversights—rate limiting, role-based access, backup plans, etc. It doesn’t catch everything, but it helps us avoid blind spots early and write clearer, faster documentation.

Result: Better scopes. Fewer surprises. Happier clients.

3. AI Can’t Replace Junior Devs (Yet)

Here’s a trap I see all the time: hiring junior devs who don’t really understand code, then expecting them to ship features by leaning on ChatGPT.

Spoiler: it doesn’t work.

We tested this. Gave a bug to both a junior dev and ChatGPT. Neither could solve it. Why? No context. No understanding of the codebase.

AI is only as useful as the person using it. You still need people who can think in systems, debug, and make trade-offs. Otherwise, you’re just babysitting an AI and praying it builds something usable.

Bottom line: AI makes good devs faster. It doesn’t make bad devs better.

4. The Tools We Actually Use

When it comes to integrating AI into our workflow, we keep it lean and practical. The core of our setup is GPT-4, which we use via ChatGPT Plus or the API.

It’s our go-to tool for everything from refactoring code to drafting scope documents and even brainstorming architecture decisions.

For in-line code generation during development, GitHub Copilot is embedded directly into our VS Code environment—it speeds up writing repetitive code like utility functions or component scaffolding without interrupting flow.

Documentation is another area where AI shines. We use Notion AI to clean up messy notes and convert rough outlines into clear, structured documents. It’s like having a junior product manager helping with clarity and polish.

Another tool that’s been a game-changer is Cursor.sh—a GPT-powered code editor designed for devs. It’s fast, contextual, and smart enough to understand most of what we throw at it during early prototyping or debugging.

We’ve experimented with tools like Claude and Gemini for brainstorming or research, but when it comes to heavy code-related tasks, GPT-4 is still the most reliable. For most small teams, just combining GPT-4 with a developer who knows their stuff will get you 80% of the way there.

5. Real Results (What Changed for Us)

The impact of AI on our productivity has been very real—and measurable. Since adopting it across our engineering process, we’ve cut our scope document writing time by about 50%.

What used to take days now takes hours, allowing us to respond to clients faster and with more confidence.

On the frontend side, builds are now 30 to 40 percent faster. Developers spend less time on boilerplate and more time solving meaningful problems, like optimizing UX performance or refining backend logic.

What’s most exciting is that we’ve been able to ship more projects without increasing our team size or cutting corners.

Our engineers are more focused, our deliverables are more polished, and we’re consistently hitting tighter timelines.

This isn’t about replacing human talent—it’s about removing friction from the development process. We’re not cutting headcount.

We’re amplifying it. By giving our team the right AI tools, we’ve unlocked more speed, better quality, and a whole new level of creative problem-solving.

A Quick Story: From Skeptic to Supercharged

One of our engineers, Jason, didn’t want to use AI. He was worried it would make him lazy. So I told him: “Try it for one week—just for repetitive stuff.” Next Monday, he came in and said, “I saved 7 hours. I finally had time to optimize the backend.” That’s the goal. AI didn’t replace him—it freed him to do deeper work.

Final Thoughts: Use AI, But Use It Right

AI is not your team. It’s a tool. If you know how to use it with intention and discipline, it can make your developers unstoppable.

But if you hand over the wheel without oversight, you’re asking for broken code, missed deadlines, and technical debt.

Don’t fear AI. Don’t worship it. Use it wisely.