AI Helped Me Build It…But I Don’t Know How It Works. Sound Familiar?
- Mandy Lee
- 12 hours ago
- 3 min read
If you’ve tried “vibe coding”, you’ve probably had this moment:
You type in a prompt.
The AI spits out a few hundred lines of code.
You run it. It works.
And then you pause.
Because while the code runs, you don’t understand what it’s doing.
That uneasy feeling? It’s not just you. It’s the heart of the growing trust gap with AI-generated code. The potential of these tools in the hands of domain experts is exciting, until we move beyond the prototype into production.
Why Understanding Matters More Than Speed
For quick prototypes, speed can be enough. But when it comes to business-critical systems, speed without understanding isn’t an advantage; it’s a liability.
That’s where the hidden costs show up:
Business logic errors slip past tests because the AI interpreted a requirement differently than you intended
Your automation works perfectly most of the time, until it doesn’t. You don’t know how or why errors occur and you lose trust in the system
You end up interrupting developers anyway, the thing you were trying to avoid
It’s not enough that the code runs or even passes tests. If you don’t know why it works, you can’t be sure it will keep working tomorrow. You can’t find and fix errors. You can’t make changes as new requirements emerge.
Trust requires understanding.
What Real Assistive AI Should Do
At Leapter, we believe assistive AI shouldn’t just generate code for you. It should help you build systems you can actually understand and trust.
That’s why instead of dumping out opaque code, Leapter generates executable blueprints: structured, visual diagrams that show exactly how your system works.
With Leapter, you can:
See every logic path — no hidden branches or black-box assumptions.
Validate intent early — confirm that what’s being built matches what the business actually meant.
Collaborate across roles — domain experts, product managers, and engineers can all understand the same blueprint.
Ship with confidence — because the deployed code is simply the verified blueprint, converted into software.
The Difference in Practice
Traditional AI tools:
Output raw code.
Manual testing takes time and automated testing requires more “vibe code”.
Business stakeholders only discover misalignments once the app is running.
Iteration is slow because you don’t know exactly how to or where to fix the problems, only that the problem exists.
Leapter:
Outputs a blueprint first — a visual, auditable map of the logic.
Everyone agrees on the design and the implementation because it’s presented in a way everyone can understand
All stakeholders are able to verify the code (the blueprint) before it is deployed.
Iteration is fast because domain experts can “play” with their ideas directly in the system, without waiting on developers or another long cycle.
It’s the difference between hoping the AI guessed correctly and knowing the system matches your shared intent.
Why This Matters Now
The danger is clear: if teams adopt “vibe coding” AI tools without solving the trust gap, they risk pushing more bugs, misalignments, and hidden flaws into production.
And the missed opportunity is just as important. Without a trustworthy way for domain experts to work directly with AI, teams lose the chance to move faster and more autonomously. When non-developers can safely test ideas and shape logic themselves, iteration speeds up across the business—not just in engineering.
We need tools that can balance the need for security and reliability whilst also empowering your domain experts to interact with code in a new way, removing the costly handoffs. The specification, driven by the business owner, becomes the code that runs in production.
Software is moving faster than ever. The only way to keep up is to make sure understanding scales with speed—and that the ability to act isn’t locked inside the developer workflow alone.
The Bottom Line
If AI helps you build something you don’t understand, it isn’t really helping. It’s just moving the bottleneck further down the pipeline—and increasing the risk of hidden flaws, rework, and missed intent.
But the upside is just as powerful. We can start to reimagine the entire software development lifecycle if we’re bold enough to throw out the “old way” of doing things. But we must do so with shared understanding as the foundation.
That’s the difference between code you hope works and systems you know you can trust.
That’s the Leapter way.