What is Vibe Coding?

“Vibe coding” is one of those internet-born terms that sounds fuzzy on purpose, but it actually describes a very real shift in how people write software.

Vibe coding = coding by intent and feel, not line-by-line precision.

Instead of carefully planning architecture and typing every detail yourself, you:

• Describe what you want in natural language
  
• Let AI (like ChatGPT, Copilot, Cursor, etc.) generate most of the code
  
• Focus on whether the result “feels right” and works, not whether you authored every line

Think of it as directing the code rather than hand-crafting it.

What vibe coding looks like in practice

• “Build me a simple payroll dashboard with employee search and export to CSV.”
  
• Skim the generated code
  
• Run it
  
• Tweak prompts like: “Make it faster,” “Clean this up,” or “This feels clunky,simplify it”

You’re judging output by vibes: usability, clarity, speed, and outcome.

Why people are talking about it

• AI can now generate large, working chunks of code
  
• Non-traditional developers (analysts, consultants, ERP folks 👀) can build real tools
  
• Speed matters more than perfection in many business contexts

The trade-offs (important)

Pros

• Extremely fast prototyping
  
• Lower barrier to entry
  
• Great for internal tools, demos, and automation

Cons

• You may not fully understand the code
  
• Harder to debug or scale long-term
  
• Risky without fundamentals (security, performance, data integrity)

A good mental model

• Traditional coding: architect + builder
  
• Vibe coding: product manager + creative director.
  
• Best engineers today: both

Can you show me an example of vibe coding vs traditional coding?

Let’s use a realistic business example:

Create a small app that calculates an employee’s net pay from gross pay.

Traditional coding (hands-on, precise)

Step-by-step thinking

• Define inputs
  
• Write logic
  
• Handle edge cases
  
• Test manually

Code (Python example):

def calculate_net_pay(gross_pay):

    tax_rate = 0.22

    benefits_rate = 0.05

    tax = gross_pay * tax_rate

    benefits = gross_pay * benefits_rate

    net_pay = gross_pay – tax – benefits

    return round(net_pay, 2)

print(calculate_net_pay(5000))

What’s happening

• You design the logic
  
• You own every line
  
• You know exactly why it works

This is solid, reliable, and maintainable,but slower to produce.

Vibe coding (intent-first, outcome-driven)

Prompt to AI

Create a simple Python function that calculates net pay from gross pay.

Assume 22% tax and 5% benefits.

Round to two decimals.

AI-generated result

def net_pay(gross):

    return round(gross * 0.73, 2)

You run it. It works.

You didn’t think about intermediate variables or structure,you trusted the vibe.

Then you refine the vibe

This feels too opaque. Make it more readable and add comments.

AI updates the code accordingly.

Key difference side-by-side

Where vibe coding shines (especially for you)

• Internal payroll tools
  
• ERP prototypes
  
• Data cleanup scripts
  
• Proof-of-concept dashboards
  
• “I need this by tomorrow” work

Where it doesn’t

• Core financial engines
  
• Security-sensitive systems
  
• Highly regulated production code (without review)

The real power move

Use vibe coding to get to 80% fast, then switch to traditional coding to harden it.

Can you map this to ERP consultants becoming builders?

Got it , let’s do a clean, side-by-side example with a slightly richer scenario so the contrast really pops.

Scenario

Build a small script that reads a list of employee hours and calculates weekly pay (including overtime).

Traditional coding

You think first, then code.

Mental steps

• Define overtime rule (over 40 hours)
  
• Write loop
  
• Handle math explicitly
  
• Make it readable and testable

def calculate_weekly_pay(hours_list, hourly_rate):

    total_pay = 0

    for hours in hours_list:

        if hours > 40:

            overtime_hours = hours – 40

            regular_pay = 40 * hourly_rate

            overtime_pay = overtime_hours * hourly_rate * 1.5

            total_pay += regular_pay + overtime_pay

        else:

            total_pay += hours * hourly_rate

    return round(total_pay, 2)

hours = [38, 42, 45]

rate = 25

print(calculate_weekly_pay(hours, rate))

Characteristics

• You designed the logic
  
• You control structure and edge cases
  
• You fully understand how it works

Reliable, but slower to produce.

Vibe coding

You describe the outcome and steer.

Prompt to AI

Write a Python function that calculates weekly pay for a list of employees.

Overtime is paid at 1.5x after 40 hours.

Return the total weekly payroll.

AI-generated code

def payroll(hours, rate):

    return round(sum(

        (40 * rate + (h – 40) * rate * 1.5) if h > 40 else h * rate

        for h in hours

    ), 2)

You run it.

It works.

You didn’t plan loops or variables , you judged it by “does this feel correct and useful?”

Then you adjust the vibe

This is hard to read. Rewrite it more clearly with comments.

AI refactors it.

What actually changed

The key insight

Vibe coding doesn’t remove thinking , it changes when thinking happens.

• Traditional: think → code → test
  
• Vibe coding: describe → run → judge → refine

The strongest developers today move fluidly between both.