When AI Fills the Gaps, You Should Think Through

TL;DR: Workslop happens when you accept AI-generated code that looks fine but lacks understanding, structure, or purpose.

Problems 😔

Solutions 😃

  1. Validate generated logic in real-world scenarios
  2. Rewrite unclear parts
  3. Add domain meaning
  4. Refactor the structure for clarity
  5. Add a human peer review
  6. Clarify the context

If you want, I can create a full list of 25+ solutions to completely fight workslop in teams and code.

Refactorings ⚙️

https://hackernoon.com/improving-the-code-one-line-at-a-time?embedable=true

https://hackernoon.com/refactoring-005-replace-comment-with-function-name?embedable=true

https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true

https://hackernoon.com/refactoring-032-apply-consistent-style-rules?embedable=true

https://hackernoon.com/refactoring-016-building-with-the-essence?embedable=true

Context 💬

You get "workslop" when you copy AI-generated code without understanding it.

The code compiles, tests pass, and it even looks clean, yet you can’t explain why it works.

You copy and paste code without reviewing it, which often leads to catastrophic failures.

https://hackernoon.com/from-helpful-to-harmful-how-ai-recommendations-destroyed-my-os?embedable=true

Sample Code 📖

Wrong ❌

def generate_invoice(data):
    if 'discount' in data:
        total = data['amount'] - (data['amount'] * data['discount'])
    else:
        total = data['amount']
    if data['tax']:
        total += total * data['tax']
    return {'invoice': total, 'message': 'success'}

Right 👉

def calculate_total(amount, discount, tax):
    subtotal = amount - (amount * discount)
    total = subtotal + (subtotal * tax)
    return total

def create_invoice(amount, discount, tax):
    total = calculate_total(amount, discount, tax)
    return {'total': total, 'currency': 'USD'}

Detection 🔍

You feel like the code "just appeared" instead of being designed.

Tags 🏷️

Level 🔋

Why the Bijection Is Important

When you let AI generate code without verifying intent, you break the bijection between your MAPPER and your model.

The program stops representing your domain and becomes random syntax that only simulates intelligence.

AI Generation 🤖

This is an AI-specific code smell.

AIs can produce large volumes of plausible code with shallow logic.

The result looks professional but lacks cohesion, decisions, or constraints from your actual problem space.

AI Detection 🧲

You can also use AI-generated code detectors.

AI can highlight missing edge cases, repeated logic, or meaningless names, but it can’t restore the original intent or domain meaning.

Only you can.

Try Them! 🛠

Remember: AI Assistants make lots of mistakes

Suggested Prompt: Give more meaning to the code

Without Proper Instructions

With Specific Instructions

ChatGPT

ChatGPT

Claude

Claude

Perplexity

Perplexity

Copilot

Copilot

You

You

Gemini

Gemini

DeepSeek

DeepSeek

Meta AI

Meta AI

Grok

Grok

Qwen

Qwen

Conclusion 🏁

Workslop smells like productivity but rots like negligence.

You protect your craft when you question every line the machine gives you. Think, design, and own your code.

Remember, YOU are accountable for your code. Even if Artificial Intelligence writes it for you.

Have you noticed the copied and pasted text above?

If you want, I can create a full list of 25+ solutions to completely fight workslop in teams and code.

https://hackernoon.com/code-smell-06-trying-to-be-a-clever-programmer?embedable=true

https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxx?embedable=true

https://hackernoon.com/code-smell-273-overengineering?embedable=true

https://hackernoon.com/code-smell-238-dealing-with-entangled-code?embedable=true

https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity?embedable=true

Disclaimer: Code Smells are my opinion.

Feature image by ZHENYU LUO on Unsplash


The most disastrous thing you can ever learn is your first programming language.

Alan Kay

https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true


This article is part of the CodeSmell Series.

https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true