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 😔

• Hollow logic
• Unclear or ambiguous intent
• Misleading structure
• Disrespect for human fellows
• Missing edge-cases
• Fake productivity
• Technical debt

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 ⚙️

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.

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 🔍

[X] Manual

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

Tags 🏷️

• Declarative Code

Level 🔋

[x] Intermediate

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 a specific AI 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.

Relations 👩‍❤️‍💋‍👨

More Information 📕

Workslop (in spanish)

Harvard Review

Disclaimer 📘

Code Smells are my opinion.

Credits 🙏

Photo by ZHENYU LUO on Unsplash

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

Alan Kay

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This article is part of the CodeSmell Series.