Vibe Coding Introduction

The Non-Programmer Who Built Software

A marketing director at a mid-sized company needed a specific tool: something that would monitor competitor websites for pricing changes and send alerts when competitors adjusted their positioning. She looked at off-the-shelf solutions. Too expensive for her budget. Too generic for her specific needs. Not quite what she wanted.

In the past, this would have been the end of the story. She wasn’t a programmer. Building custom software wasn’t something she could do. She’d have to make do with inadequate tools or wait months for IT to prioritize her request.

Instead, she spent a weekend with an AI coding assistant. She described what she wanted. The AI generated code. She tested it. It didn’t work right. She described what was wrong. The AI fixed it. She tested again. A few more iterations. By Sunday evening, she had a working tool that monitored competitor pages and sent her Slack alerts when prices changed.

She’d created software without knowing how to code.

This chapter introduces “vibe coding”—creating software by describing what you want and iterating with AI assistance. It’s not traditional programming. It’s not drag-and-drop no-code tools either. It’s a new middle ground where domain experts can build custom tools by leveraging AI’s ability to generate and debug code.

Part 7 of this book shifts from using AI for workflows to creating with AI—building tools, applications, and presentations that extend your capabilities beyond what existing software provides. This chapter covers the mindset and boundaries. The following chapters cover practical implementation.

What Is Vibe Coding?

Vibe coding is creating software by describing functionality to AI and iterating on results. The term captures something important: you’re communicating the vibe—the feel, the intent, the desired outcome—rather than writing technical specifications or code.

The Division of Labor

In vibe coding, human and AI contribute different strengths:

The human provides: - Clear description of desired outcome - Domain knowledge about the problem being solved - Judgment about whether the result is working - Direction for iterations and refinements

The AI provides: - Code generation based on descriptions - Technical implementation details - Debugging assistance when things don’t work - Architecture suggestions and alternatives

This division leverages what each party does best. You know your problem deeply. AI knows how to write code quickly. Together, you can build things neither could create alone.

Not Traditional Coding

In traditional coding, the human writes every line, understands every construct, and makes every technical decision. The coder must know syntax, algorithms, data structures, and the details of whatever languages and frameworks are being used.

Vibe coding is different. The human doesn’t write code—they direct its creation. They describe what they want, evaluate whether they got it, and guide refinements. Technical implementation happens in the AI’s generation, not in the human’s head.

This doesn’t mean vibe coding is easier than traditional coding. It requires different skills: clarity of communication, rigorous testing, good judgment about what’s working, and patience with iterative refinement. These skills are demanding but different from traditional programming skills.

Why “Vibe”?

The term “vibe coding” captures the communication style involved. You’re not specifying algorithms or data structures. You’re describing what something should feel like, what it should accomplish, what outcome you want. The AI translates your description—your vibe—into technical implementation.

“I want a tool that feels snappy, shows me the most important information first, and alerts me when something needs attention.” That’s a vibe. The AI figures out how to implement snappiness, prioritization, and alerting.

When Vibe Coding Works

Vibe coding isn’t appropriate for everything. Understanding when it works—and when it doesn’t—helps you apply it successfully.

Good Candidates for Vibe Coding

Personal and team tools. Tools you build for your own use or your team’s use are ideal starting points. You’re the user, so you understand the requirements deeply. You can iterate rapidly because you’re not coordinating with external stakeholders. Failure affects only you.

Clearly defined requirements. Vibe coding works best when you can describe success precisely. “A tool that reads CSV files and identifies duplicate rows based on email or phone” is specific enough to build. “A customer management system” is too vague—it could mean anything.

Lower stakes projects. When failure is recoverable and iteration is cheap, vibe coding shines. Proof of concepts, internal utilities, data transformation scripts—projects where you can experiment, fail, learn, and try again without major consequences.

Data transformation and analysis. Many useful tools take data in one form and produce it in another: parsing files, generating reports, calculating metrics, formatting outputs. These well-bounded problems are excellent for vibe coding.

Poor Candidates for Vibe Coding

High reliability requirements. Mission-critical systems, healthcare applications, financial transactions, safety systems—anything where failure has serious consequences isn’t appropriate for vibe coding by non-programmers. These systems need professional engineering with appropriate testing, review, and accountability.

Complex architecture. Large-scale distributed systems, applications with many external integrations, performance-critical services—these require architectural expertise that vibe coding doesn’t provide. The AI can generate code, but it can’t design complex systems reliably.

Professional software products. If you’re building software to sell to customers or deploy to many users, vibe coding alone isn’t sufficient. Production software needs professional development practices: security review, scalable architecture, maintenance planning, documentation.

Security-sensitive applications. Anything handling sensitive data, authentication, payment processing, or external access needs professional security review. AI-generated code can contain security vulnerabilities that non-experts won’t recognize.

The Sweet Spot

Vibe coding works best at the intersection of three conditions:

High domain expertise. You understand the problem deeply. You know what good looks like. You can evaluate whether the tool is solving the right problem.

Lower technical complexity. The solution doesn’t require sophisticated algorithms, complex integrations, or architectural decisions beyond your understanding.

Clear success criteria. You know when it works. You can test it. You can verify the output is correct.

When these three conditions align, vibe coding can produce genuinely useful tools faster than any alternative.

The Vibe Coding Process

Vibe coding follows a predictable process: describe, generate, test, iterate. Understanding this process helps you approach projects systematically.

Step 1: Clear Description

Start with a clear problem statement. The quality of your description directly affects the quality of what you get.

Good description: “I need a tool that reads a CSV file containing customer contacts, identifies rows that likely represent the same person based on matching email addresses or phone numbers, and generates a report showing which records should be merged.”

Poor description: “I need something to manage customer data.”

Good descriptions include: - What problem you’re solving - What input the tool receives - What output it should produce - What constraints or special cases matter

Invest time in crafting clear descriptions. This upfront clarity saves hours of iteration later.

Step 2: Iterative Development

Build in small pieces rather than trying to create everything at once.

Ask for one capability at a time. Test each piece before adding more. Build up from working foundations. This incremental approach has several advantages: easier debugging when something breaks, clearer understanding of what each part does, and ability to change direction without losing all your work.

If you ask for a complete complex tool at once, you’ll get something that might partially work in confusing ways. If you build incrementally, you’ll understand what you have and can fix problems systematically.

Step 3: Testing and Validation

You must verify that what you built actually works. This is where many vibe coding projects fail—the creator assumes the generated code works because it looks right or runs without errors.

Test with real data. Don’t just test with simple examples. Use realistic data that represents your actual use case.

Check edge cases. What happens with empty files? Missing fields? Unusual formats? Edge cases reveal problems that simple testing misses.

Verify outputs make sense. Don’t just check that output appears—verify that it’s correct. Check the math. Validate the logic. Confirm the results match what you’d calculate manually.

Don’t trust it blindly. AI-generated code can have subtle bugs that produce plausible-looking but wrong results. Your job is to catch these through systematic verification.

Step 4: Iteration and Refinement

Expect multiple rounds of refinement. The first version is rarely perfect.

When something doesn’t work, describe what’s wrong specifically. “It’s not working” doesn’t help the AI fix the problem. “The date parsing fails when dates are in MM/DD/YYYY format instead of YYYY-MM-DD” gives actionable direction.

Ask for targeted fixes rather than regenerating everything. Keep what works; improve what doesn’t. Each iteration should move you closer to a working tool.

Common Pitfalls

Scope creep. Starting simple and gradually adding “one more thing” until you have an unmanageable mess. Define boundaries early and resist expansion.

Insufficient testing. Assuming code works because it looks right or runs without errors. Test rigorously with realistic data.

Unclear requirements. Vague descriptions produce vague implementations. Invest in clarity upfront.

Giving up too early (or too late). Some projects fail because people quit before the solution stabilizes. Others fail because people keep iterating on fundamentally flawed approaches. Learn to recognize which situation you’re in.

What You Need to Understand

You don’t need to be a programmer, but you need some foundational understanding to vibe code effectively.

Conceptual Understanding

You need to understand basic concepts, even if you can’t implement them:

Functions are reusable pieces of logic. When the AI creates a function to calculate something, you should understand that it can be called from multiple places.

Variables are containers for data. When the AI stores something in a variable, you should understand it’s being saved for later use.

APIs are ways to connect to external services. When your tool needs to send Slack messages or read from a database, it’s using APIs.

Databases provide structured data storage. Understanding the difference between storing data in a file versus a database helps you communicate requirements.

You don’t need to implement these concepts, but understanding them helps you communicate effectively and recognize when something is wrong.

Reading Code

You need to be able to scan code and roughly understand its structure, even if you couldn’t write it yourself. This helps you:

  • Verify the AI is attempting what you asked
  • Spot obvious errors or missing pieces
  • Understand what’s happening when something breaks
  • Know when something looks wrong even if you can’t fix it

Systematic Testing

Perhaps the most important skill is systematic testing. You need to verify that generated code actually works, which means:

  • Defining what success looks like before testing
  • Creating test cases that cover normal and edge conditions
  • Verifying results are actually correct, not just present
  • Documenting what works and what doesn’t

The Collaboration Model

Think of the AI as a very fast but error-prone junior developer. It can generate code rapidly but makes mistakes that need catching. It needs clear direction but can suggest approaches you hadn’t considered. It improves with feedback but requires your judgment to ensure quality.

This mental model helps you approach vibe coding with appropriate expectations. You’re not writing code yourself, but you’re not passively receiving perfect solutions either. You’re collaborating—providing direction, evaluating results, and guiding refinement.

Getting Started

Choosing the right first project significantly affects your success.

Ideal First Project Characteristics

Your first vibe coding project should be:

Real. Solve an actual problem you have. Theoretical exercises don’t motivate the persistence that vibe coding requires.

Bounded. Clear beginning and end. Definable success criteria. Resistance to scope expansion.

Low stakes. Failure should be learning experience, not disaster. Avoid anything where failure has significant consequences.

Personal. You’re the user. You understand the requirements. You can iterate quickly without external coordination.

Setting Expectations

Your first project will take longer than you expect. The learning curve is steeper than it appears. Most people underestimate testing time. This is normal.

But success builds capability. Each project teaches skills that transfer to the next one. The second project goes faster than the first. The third faster still.

Common Objections

“I’m not technical—I can’t do this.”

Vibe coding is specifically designed for domain experts who aren’t traditional programmers. Your deep knowledge of the problem you’re solving is the key ingredient. The AI provides technical implementation; you provide problem understanding and quality judgment.

“Isn’t this just a recipe for bad software?”

It can be—if you skip testing and verification. But the same is true of code written by professionals. Quality comes from rigorous verification and iteration, not from how the code was generated.

“Should I just hire a developer instead?”

Sometimes yes. For complex systems, professional software products, or security-sensitive applications, professional development is appropriate. But for personal tools, proofs of concept, and internal utilities, vibe coding is faster and cheaper.

“What if the AI makes security mistakes?”

This is a real concern. For anything involving sensitive data or external exposure, get professional review. Vibe coding is for personal and internal tools where you control the risk, not for production systems handling sensitive information.

“What about maintenance? Code needs to be maintained.”

True. AI-generated code still needs maintenance when requirements change or problems emerge. For personal tools with limited scope, maintenance burden is manageable. For larger projects, factor maintenance into your decision. Sometimes the answer is: build it with vibe coding to prove the concept, then rebuild professionally if it becomes critical.

“Will this get me in trouble with IT or compliance?”

Possibly, depending on your organization’s policies. Many organizations have rules about what software employees can create and deploy. Check your policies. For personal productivity tools that don’t touch sensitive data, you’re usually fine. For anything that connects to company systems or processes company data, get appropriate approvals.

Your Monday Morning Action Item

Identify one potential vibe coding project this week:

Step 1: List your friction points. What manual processes frustrate you? What tools don’t exist that you wish did? What data transformations do you perform repeatedly?

Step 2: Evaluate fit. Is the problem clearly definable? Is the scope containable? Are stakes low enough for experimentation?

Step 3: Write the project description. In 2-3 sentences: What does the tool do? What input does it take? What output does it produce?

Step 4: Assess your readiness. Do you have access to an AI coding assistant? Do you have time for iteration and testing? Are you prepared to learn through trial and error?

Don’t build yet—just identify and describe. Chapter 21 covers the actual building process. For now, find a project where your domain expertise matters more than technical complexity. The goal is choosing something that genuinely helps you while being bounded enough to complete successfully.