One of the most fascinating (and concerning) shifts in software development lately is how quickly Large Language Models (LLMs) have gone from novelty to everyday tool. They’re now a staple in the toolkit of junior developers, startups, and even large engineering teams.
Used wisely, they can accelerate prototyping, fill gaps in boilerplate code, and help clarify requirements. Used recklessly, they can turn codebases into ticking time bombs of complexity.
From Mental Models to Vibe Coding
When experienced engineers tackle a problem, they carry a clear mental model: what the software is supposed to do, how the code actually behaves, and where the two differ. They iterate deliberately, testing, refactoring, and deciding whether to fix the code or revisit the requirements.
Many LLM users skip that discipline entirely. They prompt, paste, and pray—letting the AI “vibe” its way to a solution that looks right in the moment. When tests fail, the instinct is often to re-prompt until something compiles, not to understand why it works.
This “vibe coding” is fast, seductive, and dangerous.
The Bloat Problem
LLMs don’t feel the weight of a codebase over time. They aren’t bothered by adding another dependency, copying a chunk of code instead of refactoring it, or using a library for a two-line function.
The result? A web of code that’s bigger than it needs to be, harder to maintain, and littered with hidden complexity. Every “quick fix” that skips deliberate design decisions compounds into long-term technical debt.
And because the AI doesn’t maintain a persistent understanding of the system as a whole, each round of changes risks introducing subtle inconsistencies. The more you lean on it, the more tangled things get.
LLMs Cleaning Up LLMs
When the inevitable bugs and performance issues surface, many teams turn back to the LLM for help…
ironically asking the same tool that created the mess to untangle it.
It’s a vicious cycle:
- AI generates bloated or inconsistent code.
- AI is asked to “fix” it, often layering on patches instead of true refactoring.
- The codebase grows heavier, harder to reason about, and riskier to change.
Wash. Rinse. Repeat.
Over time, MVP / prototypes meant to last weeks or months become full-fledged production systems, complete with quirks, inefficiencies, and architectural shortcuts that were never meant to scale.
A Better Approach
LLMs have a clear place in modern development:
- Prototyping – Quickly explore ideas and validate feasibility.
- MVP Development – Build small, simple solutions to test with real users.
- Boilerplate & Repetitive Code – Automate the grunt work so engineers can focus on high-value problems.
Once a project grows beyond that, it needs the steady hand of experienced engineers, people who know how to keep systems lean and scalable, and who can design for growth without bloat. These are the same engineers increasingly tasked with cleaning up the tech debt left behind by vibe coders and prototypes that went rogue.
What Now?
If your product started as a quick prototype but is now straining under its own weight, it’s not too late. Connect with our team to turn your great idea into a lean, maintainable, growth-ready system. We’ll help you untangle the web, pay down the tech debt, and set you up for long-term success.
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