The 2,000-line stress test: How we tried to break AI software development

It all started with a simple question during our R&D meeting: “If we took the brakes off AI development completely, how fast could we actually go?”

We weren’t looking to cut corners on client work. We wanted to stress-test the limits of AI-assisted development in a controlled environment. So, we designed an experiment. We tasked one of our senior architects with a challenge: rebuild a complex authentication and user management module using AI as the primary driver, with speed as the only KPI.

For three weeks, it felt like we had discovered a cheat code. Features that usually took days appeared in hours. The daily stand-up was full of wins. The velocity metrics were off the charts.

But then came the code review. Which was a fascinatingly complex disaster. We opened a single file that had ballooned to 2,247 lines of TypeScript. It was a monolithic nightmare where business logic was tangled with UI rendering and raw SQL strings were mixed comfortably with ORM calls.

The AI had hallucinated three different error-handling patterns in the same module. It had implemented security checks that looked correct on the surface but were fundamentally flawed, checking passwords with simple string comparisons instead of hashing. We realized we had proven a fundamental law of this new era: AI doesn’t create bad code, it creates chaos at scale.

That experiment became the foundation of our new methodology. We spent the next six months deconstructing that failure to build a framework that actually works.

We compiled every lesson, template and checklist from that journey into a comprehensive guide which you can access completely free.

The trap of “vibe coding”

The mistake we made is common. We call it the “Vibe coding trap”.

Vibe coding happens when you treat AI like a senior developer instead of what it actually is: an incredibly fast pattern matcher with no understanding of consequences. You start with a prompt; the AI gives you working code and your brain releases a hit of dopamine. It feels like progress.

But essentially, you are accumulating compound interest on technical debt at a rate of 50% daily. Every prompt you send without architectural context adds a layer of assumptions. The AI invents a database schema one day and a different one the next.

We need to stop treating AI as a magic wand and start treating it as a power tool. You don’t let a power tool decide where to build the wall, you just use it to drive the screws.

The solution: The architecture-first approach

The breakthrough came when we stopped asking AI to “build features” and started giving it rigid constraints. We call this the Architecture-first approach of AI development.

Think of building a house. You don’t let the contractor decide if the house should have a basement or where the kitchen goes. Those are expensive, irreversible decisions. But once the foundation is poured and the walls are up, you want them working as fast as possible.

In software terms, your database schema, authentication strategy and API style are the foundation. We learned to create an “Architecture lock file”, a constitution for our codebase that explicitly states what AI can never change .

When AI operates within those locked constraints, the quality of the output skyrockets. Instead of “building a user system,” you are asking it to “implement a user profile using our existing Prisma schema and Fastify routes.” The difference is night and day.

If you are curious about the architecture-first approach, it’s all inside our free eBook.

The “no-go zones”: What AI should never touch

Part of our recovery involved identifying specific categories of problems that AI should never attempt to solve. These are areas where a mistake might lead to a lawsuit or a security breach.

Here are two of our biggest “red zones”:

Authentication: After the 2,000-line disaster, we established a rule: AI never generates auth logic. It doesn’t understand the nuance of session management or token refreshing. If you let AI handle this, it might implement authorization bypasses that look perfectly legitimate during a code review.
Payment processing: We have seen teams let AI generate Stripe integrations where the API calls worked, but the code violated PCI compliance in a dozen ways. AI simply doesn’t understand the legal requirements of storing payment data.

By fencing off these dangerous areas, we actually made our team faster. We stopped wasting time fixing dangerous AI code and let the AI focus on what it’s good at: business logic, UI components and data transformation.

The hidden benefit: Doing the “boring stuff”

Once we stabilized our coding process, we discovered that the real magic of AI was in handling the entire development lifecycle.

Documentation: We now have AI update our API documentation and README files as the code is being written.
Traceability: We use AI to map requirements to code modules, catching 60% fewer production bugs related to missed requirements.
Testing: We generate comprehensive test scenarios for edge cases we wouldn’t have even thought to test manually.

From chaos to control

The transition from “vibe coding” to a structured, AI-assisted workflow didn’t happen overnight. It took us weeks of trial and error, difficult retrospectives and refining our prompt engineering.

But you don’t have to learn the hard way. We have documented our entire journey, including the failures, the specific tech stack we use, and the “verification checkpoint system” that prevents bugs from hitting production.

We put everything, our templates, our checklists and our 30-day implementation roadmap, into a comprehensive guide.

Stop building chaos. Start building an engine. Download the “How to Efficiently Use AI in Software Development” eBook for free.

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