What Is Vibe Coding? Why AI-Generated Projects Have Poor Documentation
I discovered OpenClaw through a Reddit post. The project looked impressive - an AI agent that could automate complex workflows. But when I tried to understand how it worked, I hit a wall.
The documentation read like it was generated by an LLM. Generic descriptions. No concrete examples. Missing critical details about configuration. I spent two hours trying to set up a basic workflow before giving up.
Then I found the Reddit comments:
> "Of course it was vibed." (20 upvotes)
> "What do you think that a 100% vibed project's author> will sit and write documentation for 40 hours? Never." (20 upvotes)
> "Even the docs were vibed?" - OP's surpriseI had to know: what is “vibe coding” and why does it produce such terrible documentation?
What Vibe Coding Actually Means
Vibe coding is building software entirely through AI prompts. You don’t write code - you describe what you want and accept what the AI produces. The “vibe” is accepting outputs that feel right without deep verification.
TRADITIONAL DEVELOPMENT┌─────────────────────────────────────────────────────────┐│ Write Code → Test → Debug → Document → Maintain ││ ↓ ↓ ↓ ↓ ↓ ││ (You own each piece, understand each decision) │└─────────────────────────────────────────────────────────┘
VIBE CODING┌─────────────────────────────────────────────────────────┐│ Prompt AI → Accept Output → Prompt AI → Accept Output ││ ↓ ↓ ↓ ↓ ││ (You skip understanding, skip ownership, skip docs) │└─────────────────────────────────────────────────────────┘This isn’t just about using AI as a tool. Vibe coding means the AI does 90%+ of the work. The developer becomes a prompt engineer rather than a programmer.
Why Documentation Dies in Vibe Coding
I’ve seen this pattern repeatedly. Here’s what happens:
1. No Human Investment
The Reddit comment captures it perfectly: a developer who vibes a project won’t spend 40 hours writing documentation. They’ll just prompt more features instead.
TRADITIONAL PROJECT (100 hours total)┌────────────────────────────────────────┐│ ████████████████████ Coding (40h) ││ ████████████ Debugging (25h) ││ ███████████ Documentation (20h) ││ ████████ Testing (15h) │└────────────────────────────────────────┘
VIBED PROJECT (20 hours total)┌────────────────────────────────────────┐│ ███████████████████ Prompting (15h) ││ ███████ Debugging (5h) ││ █ Documentation (~0h) ││ █ Testing (~0h) │└────────────────────────────────────────┘When you prompt a feature in 5 minutes instead of coding it in 5 hours, why would you suddenly spend 20 hours documenting it?
2. AI Generates Generic Content
I tried reading the OpenClaw docs. They had the distinct flavor of LLM output:
- Correct grammar but wrong specificity
- Plausible-sounding explanations that didn’t match the code
- Missing the “why” behind decisions
- No troubleshooting guidance
GENERIC AI DOCUMENTATION:"The configuration file allows you to customizevarious aspects of the application behavior.Simply modify the values to suit your needs."
HUMAN DOCUMENTATION:"Set retry_count to 3 - I tried 5 but it causedrate limiting on the OpenAI API. The default of 1fails on complex workflows that take longer than30 seconds to respond."The AI doesn’t know what you struggled with. It doesn’t know what actually works versus what should work.
3. No Ownership Means No Understanding
When I vibe code something, I understand the surface. I can make it work. But I don’t understand the depths.
KNOWLEDGE DEPTH
You write the code: ┌─────────────────────────────────┐ │ Surface: What it does │ ├─────────────────────────────────┤ │ Logic: How it works │ ├─────────────────────────────────┤ │ Edge cases: When it fails │ ├─────────────────────────────────┤ │ History: Why decisions made │ └─────────────────────────────────┘
You vibe the code: ┌─────────────────────────────────┐ │ Surface: What it does │ ├─────────────────────────────────┤ │ ??? │ ├─────────────────────────────────┤ │ ??? │ ├─────────────────────────────────┤ │ ??? │ └─────────────────────────────────┘Without deep understanding, I can’t write documentation that helps others. I only know what the code does, not how to explain it.
The Real Trade-offs
Vibe coding isn’t inherently evil. Let me show where it makes sense:
✓ APPROPRIATE FOR VIBE CODING: • Personal scripts and automations • Rapid prototypes you'll throw away • Learning new concepts • One-off data processing • Hackathon projects
✗ INAPPROPRIATE FOR VIBE CODING: • Production systems • Open-source libraries • Team projects • Long-term maintenance • Anything requiring documentationThe problem emerges when people publish vibed projects as open source. The code might work. The features might be impressive. But the project lacks the human investment that makes software maintainable.
VIBED PROJECT AFTER 6 MONTHS:┌──────────────────────────────────────────────────────┐│ ││ Original author: "I don't remember how this ││ works, let me check the docs... oh wait." ││ ││ New contributor: "The docs say to do X, but ││ that doesn't work. There's no examples." ││ ││ Result: Project abandoned ││ │└──────────────────────────────────────────────────────┘What I’m Doing Differently
After experiencing the OpenClaw documentation problem, I’ve set rules for my AI-assisted projects:
- Document while vibing: If I prompt a feature, I immediately write down what it does and why
- Code review myself: I read through generated code before accepting it
- Add context manually: I insert comments about decisions AI couldn’t know
- Test manually: I don’t trust AI-generated tests alone
MY HYBRID WORKFLOW┌─────────────────────────────────────────────────────────┐│ ││ 1. Prompt AI for initial implementation ││ ↓ ││ 2. READ the code (understand before accepting) ││ ↓ ││ 3. MANUALLY add documentation for key decisions ││ ↓ ││ 4. WRITE examples of how to use it ││ ↓ ││ 5. TEST the actual use cases ││ │└─────────────────────────────────────────────────────────┘This takes longer than pure vibe coding. But it produces something I can maintain.
The Future of Vibed Software
The term “vibe coding” will likely disappear as AI assistance becomes standard. But the documentation problem won’t.
I predict we’ll see more projects with:
- Feature-rich codebases
- Surface-level documentation
- Missing architectural decisions
- No troubleshooting guidance
- Abandoned maintainership
The solution isn’t to stop using AI. It’s to recognize that AI can write code but can’t own it. Documentation requires human experience - the mistakes, the debugging sessions, the “why did I do this?” moments.
When you vibe code a project, you’re skipping those experiences. That’s fine for personal use. But publishing it without documentation means passing your skipped learning to others.
Related Knowledge
The concept of “vibe coding” emerged from the AI developer community around 2024-2025 as tools like Claude, ChatGPT, and GitHub Copilot became capable of generating entire projects. The term appears frequently on Reddit communities like r/AI_Agents and r/LocalLLaMA.
Documentation quality has always correlated with ownership. Open source research shows projects with multiple maintainers have better documentation than single-author projects. Vibe coding extends this - when the primary author doesn’t understand the code deeply, documentation suffers.
AI-generated documentation often fails the “specificity test.” LLMs produce grammatically correct but context-free content. Good documentation includes specific examples, edge cases, and explanations of why decisions were made - things AI cannot know without human input.
Summary
In this post, I explained what vibe coding is - building software entirely through AI prompts without manual code writing. The documentation problem stems from three factors: developers won’t invest 40 hours writing docs for projects they vibed in 20 hours, AI generates generic content without project-specific context, and without code ownership comes no deep understanding needed to explain the code.
Vibe coding works for personal projects and prototypes. But publishing vibed projects without human-quality documentation passes the cost of skipped understanding to users.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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