In today’s fast-paced dev environments, a new style of programming is emerging, Vibe Coding. If your team is embracing LLMs in the dev workflow, the real question isn’t “Can we ship faster?”, it’s “Can someone else ship this six months from now?” While I love the idea of Vibe Coding, I think we must remember to build with future developers in mind.
💡 What Is Vibe Coding?
Vibe coding refers to AI-assisted, flow-state software development where large portions of code are generated from natural language prompts to LLMs. Developers prioritize speed, creativity, and experimentation, often with minimal human review or traditional documentation practices.
It feels magical. You speak, code appears. But is it sustainable?
📉 Documentation & Maintainability Struggles
As energizing as vibe coding can be, the byproduct is often opaque, unmaintainable codebases. Here’s why:
In vibe coding environments, several documentation and maintainability issues frequently arise. Inline code comments are rarely included, making the code difficult to understand or debug later. High-level documentation, such as README files or explanations of architectural rationale, is often missing, which hampers onboarding and causes the original design intent to be lost. Naming conventions and function boundaries tend to be inconsistent, increasing the risk during refactoring or scaling. Finally, documentation of design decisions is seldom captured, allowing technical debt to accumulate rapidly. These patterns reveal a critical gap in sustainability when using AI-assisted development without intentional oversight.
Without intentional guardrails, teams quickly trade speed for sustainability.
🛠 Current Mitigations & Emerging Practices
Despite these risks, best practices are evolving:
Manual Review Is Non-Negotiable
Every AI-generated code snippet should be reviewed as if it were from a junior dev. Ask: "Would this make sense to someone reading it six months from now?"
Set Documentation Standards Early
- Require docstrings, consistent naming, and architectural overviews. Make documentation a gating factor in code review—not a nice-to-have.
Use AI for Post-Gen Summaries
Tools like GPT can help explain what the code does after it’s been written.
Developers can prompt AI to generate function summaries, rationale, or even README drafts. It’s not perfect, but it’s a step in the right direction.
Explore Experimental Tooling
- Frameworks like VIBE4M aim to automatically validate the completeness of documentation and consistency of naming in AI-generated projects. Still early, but promising.
📌 Key Takeaways
Vibe coding doesn’t naturally produce good documentation.
Without enforcement, inline comments, system overviews, and decision logs are typically missing.
Documentation debt becomes technical debt.
Projects suffer long-term if teams don’t deliberately slow down to explain the "what" and the "why."
Treat vibe coding as a tool, not a shortcut.
AI can accelerate development, but it still needs human judgment, clear conventions, and robust review processes.
💬 Have you worked on a vibe-coded project? Did it come with enough documentation? Or did you end up reverse-engineering it from scratch? Drop your experiences below—let’s discuss!
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