Why your AI suddenly fails at coding and how to fix it

Why do AIs start doing silly things with your code?

Because they create a mess — all by themselves.

AI-generated code often starts clean and shiny, but as you ask it to “just add this one thing” or “tweak that part,” it begins stacking ideas in weird places, mixing concerns, and tangling dependencies. It doesn’t really understand your system’s structure. It just imitates patterns.

Without guidance on cohesion and coupling, the AI slowly builds a spaghetti tower: functional, maybe, but wobbly and unpredictable.

Here’s is how to keep your code understandable to Cursor, Windsurf and all the other bastards.

🍳 Think of It Like Organizing Your Kitchen

• Cohesion is like keeping all your baking ingredients together, your pots and pans in another place, and your spices on a rack. Things that are related stay together.
• Coupling is about how dependent your appliances are on each other. If your toaster is hardwired into the microwave, that’s tight coupling. If both plug in separately, they’re loosely coupled.

🧩 Cohesion: Keeping Related Things Together

High cohesion means that elements or ideas that belong together are grouped together.

When software has high cohesion, it avoids being:

• “Incoherent fragments of ideas”
• An “undifferentiated soup of ideas”

✨ Functional Cohesion

This is the most desirable kind of cohesion. It means code that works together on a single task is kept together.

This helps make the business logic – the part that solves real-world problems – easier to understand.

🧬 Informational Cohesion

This groups code based on operating on the same data. However, this can sometimes make it harder to change one thing without affecting others, which lowers what’s called Change Locality.

✅ Summary

When software elements (e.g., code, components, or services) have high functional cohesion, related tasks and logic are co-located and easier to reason about.

🔗 Coupling: Limiting Dependencies

While cohesion is about what stays together, coupling is about how much one part depends on another.

• Low coupling is ideal, because humans can only juggle so many dependencies in their heads.
• When systems are tightly coupled, changing one part means changing many others too.

💣 Real-World Example: The Monolith

A monolithic app with extremely high coupling—where shipping rates, checkouts, and everything else can call each other freely—is hard to change. It’s a classic “Big Ball of Mud”: impossible to untangle, hard to reuse, and brittle.

🌟 Why It Matters (Even If You Don’t Code)

The goal: high cohesion + low coupling. This combo has real benefits for everyone involved in software projects:

1. 🧠 Improved Understandability

• Organized systems are easier to follow.
• “Modules tell the story of the system.”

2. 🔧 Easier Maintenance & Change

• Changes are cheaper and safer.
• Change Locality means you only touch the part that matters.

Like tweaking a baking recipe without messing up the stir-fry drawer.

3. 🧪 Better Testability

• High cohesion + low coupling = more testable code
• Testable code = fewer bugs, better reliability

4. 🤝 Independent Reasoning

• Teams can understand parts of the system independently.
• Less stepping on each other’s toes.

5. 🚀 Faster Learning & Lower Risk

• Cohesive code helps devs learn faster.
• Reduces the chance of writing contradictory or broken code.

6. 🏗️ Foundation for Good Architecture

These principles scale from small components to large systems:

• Helps decide how to split up a monolith or design microservices.
• A weakly cohesive service is a candidate for decomposition.

🧼 In Summary

Striving for high cohesion and low coupling:

• Keeps software clean and organized
• Makes it easier to understand, extend, and fix
• Reduces bugs and boosts productivity
• Supports better team collaboration and faster innovation

And even if you’re not a coder, this kind of software hygiene ultimately leads to better, more adaptable, and more successful products.