← Blog

Vibe Coding vs. AI-Accelerated Development: Why Coding Fundamentals Still Matter

2026-06-28 · The Libre Team

People have been talking a lot about “vibe coding.” This is when you describe an idea to an AI assistant and let it build most or all of the app. Sometimes the results are impressive, but other times they are unstable, insecure, or just plain “AI slop.”

I use AI to speed up my own projects, so this made me ask myself: Am I a vibe coder?

I do not think using AI to write code automatically makes you a vibe coder. The real difference is whether AI is replacing the engineering process or just helping with it.

After years of building projects, studying programming, and working in the software industry, I have learned that knowing the basics still matters. In fact, it often makes AI much more helpful.

1. Programming knowledge helps you give better instructions

Modern AI tools can create something surprisingly functional from a simple prompt. However, there is a major difference between asking:

Make me an app that tracks my workouts.

And providing instructions such as:

Create a local-first workout tracker using Svelte and SQLite. Separate the interface, data access, and business logic layers. Users should be able to create exercises, record sets, and view previous workouts. Add validation and tests before implementing additional features.

The second prompt does more than just describe how the app should look. It also makes choices about how the app is built, where data is stored, how easy it is to maintain, and how it will be tested.

These choices matter because decisions made early in a project can affect everything that comes after. A prototype with a weak structure might work at first, but adding things like authentication, syncing, collaboration, or new platforms later could mean you have to rewrite a lot of code.

You do not need to be a senior software engineer to give better instructions. Just knowing the basics can make a big difference in how you guide an AI assistant, including:

  • Files
  • Functions
  • Variables
  • Databases
  • APIs
  • Version control
  • Application state

2. Basic knowledge reduces unnecessary back and forth

AI-generated code still makes mistakes. The model might misunderstand what you want, use an old library, repeat something that already exists, or even solve the wrong problem in a convincing way.

Without some understanding of the code, every problem has to be returned to the AI:

  • “It does not work.”
  • “Now I am getting another error.”
  • “That fixed the button, but the database stopped saving.”

This back-and-forth can use up your usage limits, API tokens, and, most importantly, your time.

Knowing the basics helps you figure out what actually went wrong. Instead of asking the AI to check the whole project over and over, you can point out the real problem:

The form submits correctly, but the database insert fails because this value is undefined. Check how the form data is passed into the save function.

That kind of instruction is much more helpful for debugging.

It also lets you fix simple problems on your own. AI is most helpful when it:

  • Takes care of repetitive tasks
  • Explains something new
  • Helps you find a bug
  • Gives you a starting point

It is less helpful if it has to keep figuring out your project’s basics every time.

3. Good engineering practices protect AI-assisted projects

AI can make starting a project feel really fast, but the problems usually show up later.

An app might end up with:

  • Duplicate components
  • Inconsistent names
  • Unnecessary dependencies
  • Missing error handling
  • Security problems
  • Multiple ways of accomplishing the same task

Each choice might seem fine on its own, but together they add up to technical debt.

Technical debt is work you put off for later. You can move fast now, but sooner or later, someone will have to understand, fix, or redo those rushed choices.

Basic engineering practices can prevent much of this:

  • Use Git or another version-control system.
  • Commit working changes regularly.
  • Review generated code before accepting it.
  • Test how the app actually works, not just how it looks.
  • Keep credentials and API keys out of the codebase.
  • Document important architectural decisions.
  • Add one feature at a time.
  • Create a backup before allowing an agent to make project-wide changes.

AI does not remove the need for these habits. In fact, because AI can create so much code so quickly, these practices are even more important.

4. Knowledge makes AI faster, not slower

Learning to program might seem slower at first than just asking AI to do everything. For a small weekend project, that might be true.

But for a serious project, speed is not just about how fast you get from an idea to the first working version. It also includes:

  • The time needed to fix mistakes
  • The time needed to add features
  • The time needed to understand old code
  • The time needed to recover from broken changes
  • The time required to make the application reliable enough for others

If you understand the project, you can make good decisions before problems show up. You will notice when an AI-generated solution is too complicated, when a library does not fit, or when a quick fix is about to become permanent.

That is the difference between just generating code and actually leading a software project.

So, is using AI considered vibe coding?

Sometimes.

If you are asking an AI to make decisions you do not understand, accepting every change without review, and judging success only by whether the application appears to work, that is probably vibe coding.

If you are setting requirements, choosing tools, checking results, testing how things work, and using AI to speed things up, that is AI-assisted development.

There is nothing wrong with experimenting. Vibe coding can be a fun way to try out an idea or make a quick prototype. The problem is when you mistake a convincing prototype for solid, reliable software.

Learning to code is still worth it

AI has not made learning to program pointless. Instead, it has given us a new reason to learn.

The more you understand software, the better you can use AI to build it. You will:

  • Give clearer instructions
  • Spot wrong answers
  • Fix problems faster
  • Make choices that help your projects grow

You do not have to master everything before you start building. Begin with the basics, make small projects, and use AI as a tutor and partner, not as a substitute for understanding.

Resources such as W3Schools, Codecademy, freeCodeCamp, and technical books can all help you begin.

Libre Academy offers another approach: structured, interactive coding courses with executable exercises and hidden tests. The desktop application can also turn supported PDFs, EPUBs, and documentation sites into interactive courses, allowing you to learn from materials you already have.

AI can help you launch an idea. Knowing the basics can help you build something that lasts.