Vibe Coding: Garbage In, Garbage Out
Why specificity matters when working with AI tools
After years of building software and automating workflows, one truth has become crystal clear: making boring tasks less boring is an art form. Whether it's spinning up projects with boilerplates or turning ideas into reality, efficiency in building is everything.
Then AI coding tools came along. Over the past year, the landscape has exploded with options: Cursor, Lovable, Bolt, and countless others. And honestly? They're only going to get better. But here's the thing nobody talks about enough: garbage in, garbage out. This isn't just some old-school programming mantra. It's especially true when working with LLMs and no-code tools.
The Specificity Problem
Here's an example. If you ask an LLM something vague like:
"Make me a dashboard that tracks daily tasks."
You're going to get a vague, often meh result. Generic UI, basic functionality, nothing special.
But what if the prompt looked like this instead:
"Use Next.js to create a dashboard that lets users create, track, and cancel tasks. Use Dribbble or Behance for design inspiration, follow best practices, and keep the code clean and simple. Add a checklist so users can track tasks and cross them off when done. Build it one feature at a time and keep it concise."
Now the LLM has a plan. A guide. A way to stay on track. The output? Way better. Why? Because the prompt was specific, clear, and gave it something to work with.
Do Your Homework (Even Just a Little)
Here's the main point for beginners: do a little homework. Even when leaning on AI tools or LLMs, take some time to learn the basics of a framework. Mastery isn't required. But at least understand the boilerplate. Know what each folder does, how the pieces fit together, and what the code is actually doing.
Why does this matter?
Because these tools are evolving fast. They're getting smarter and more powerful. But having even a basic understanding of what's happening under the hood makes anyone way better at using them. It becomes possible to spot mistakes. Tweak outputs. Debug when things break. And ultimately, build better stuff.
The Three Rules of Vibe Coding
1. Be Specific and Clear
When working with LLMs, specificity is everything. Don't just say what's needed. Explain how it should be built, what references to use, and what constraints to follow. The more context provided, the better the output.
2. Learn the Basics
Mastering a framework or language isn't necessary, but understanding the structure is. Know what a component is. Understand how routing works. Get familiar with the folder structure. This baseline knowledge makes anyone exponentially better at using AI tools.
3. Use References
Whether it's design inspiration from Dribbble, best practices from documentation, or a simple checklist to keep things organized, references guide the AI toward better outputs. Don't make it guess. Show it what "good" looks like.
Why This Matters Now
AI coding tools like Cursor, Lovable, and Bolt are amazing. They're democratizing software development in ways that seemed impossible a few years ago. But they're only as good as the input they receive. And whether you're a beginner or an experienced dev, the more understanding there is about what's being built, the better the results from these tools. These tools aren't replacing developers. They're amplifying what can be done. But amplification works both ways. Feed them garbage, get amplified garbage. Feed them clear, specific, well-thought-out prompts, get amplified excellence.
Play with Everything
Here's the advice: don't stick to just one tool. Play with Cursor. Try Lovable. Experiment with Bolt. Test out different AI assistants. Why use one when you can use all? Each tool has its strengths. Each one handles different tasks better. The more tools in the toolkit, the more flexible the development process becomes. And here's the thing: these tools are getting better every week. What didn't work last month might be perfect now. Stay curious. Keep experimenting.
The Bottom Line
Vibe coding isn't about letting AI do everything. It's about working with AI tools in a way that produces quality results. Be specific. Learn the basics. Use references. And don't be afraid to experiment with different tools. The future of development isn't "AI vs. developers." It's developers who know how to work effectively with AI vs. those who don't.