AI Coding Without Losing Control

📌 Context

AI has changed who can build software.

You no longer need to be a senior engineer to get something working. You can describe an idea, press enter, and watch code appear. That is powerful and also dangerous if you do not understand what the tool is actually doing under the hood.

Most failed AI-built projects do not fail because the AI is "bad at coding." They fail because the process encourages stacking unstable decisions too quickly.

This guide explains how to avoid that, and why it works, even if you are not technical.

I originally wrote this guide for myself after watching a few AI projects collapse under their own weight.

Later, I started turning the same process into a tool called PreDrafter so I would not have to enforce these steps manually every time.

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⚠️ The Core Problem

AI builds fast, but it builds literally.

An AI coding assistant does not understand your intent. It understands patterns and instructions.

If you say:

"Build a marketplace app"

You might mean:

• Start small
• Make reasonable assumptions
• Leave room to iterate

The AI hears:

• Users
• Sellers
• Products
• Payments
• Permissions
• Admin tools
• Edge cases
• Scalability

And it tries to helpfully juggle all of it at once.

That is where things go wrong.

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🏗️ Why Building Too Much at Once Breaks Things

Analogy: Renovating a House Without Letting the Concrete Set

Imagine building a house like this:

• You pour the foundation
• Before it hardens, you add walls
• Before walls settle, you add plumbing
• Before plumbing is tested, you add electrical
• Before anything is inspected, you add a second floor

Technically, progress is happening.

Practically, you have created a structure where every mistake multiplies.

AI coding works the same way.

Early decisions — file structure, data shape, assumptions — become the base for everything that follows. If they are wrong and you keep building, fixing them later becomes exponentially harder.

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🎯 The Single Most Important Rule

Reach a stable state before moving on.

A stable feature means:

• It works as intended
• You have manually tested all edge cases you can think of
• You understand how it is implemented
• You are comfortable building on top of it

Not perfect. Just reliable.

Why this matters

AI systems:

• Reuse existing patterns aggressively
• Assume existing code is correct
• Build new features on top of what is already there

If early code is messy, unclear, or incorrect, the AI does not question it — it amplifies it.

That is why reviewing after everything is built is so dangerous. By then:

• The original mistake has dependencies
• Other features rely on it
• Fixing it breaks unrelated parts of the app

At that point, the only real option is a rewrite.

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📦 What "Small, Well-Scoped Features" Actually Mean

A lot of people think they are building small — but they are not.

Too big (common traps)

• "Build a marketplace"
• "Implement user management"
• "Create an admin dashboard"
• "Add payments"

These sound reasonable, but each one hides multiple systems inside it.

The right size

A good feature:

• Has one primary user
• Has a clear start and end
• Can be described in one short paragraph
• Can be tested in minutes, not hours

Example

Instead of:

❌ Build a marketplace

Break it into:

• ✅ Seller onboarding (account creation + profile)
• ✅ Create a product listing (title, price, photos)
• ✅ View your own listings
• ✅ Buyer checkout (basic payment, no refunds)
• ✅ Admin: approve or reject listings

Each one can be built, reviewed, and stabilized independently.

This is critical: Features should stack, not intertwine.

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🧠 Why Smaller Features Are Easier for AI

AI models operate within context windows — limited memory of:

• Files
• Instructions
• Prior decisions

When a feature is small:

• The AI can "see" the entire problem
• It makes fewer assumptions
• Changes affect fewer files
• Errors are localized

When features are large:

• Context gets fuzzy
• The AI fills gaps creatively
• Code bleeds across boundaries
• Small requests cause large regressions

That is why people experience:

"I changed a button color and auth broke."

That is not randomness — it is scope creep inside the context window.

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⚠️ The Compounding Failure Effect

Why early review is non-negotiable.

Here is what happens when you do not pause to stabilize:

1. Feature A has a small flaw
2. Feature B builds on Feature A
3. Feature C assumes both are correct
4. You discover the flaw during Feature D

At this point:

• Fixing A breaks B and C
• Fixing B requires rethinking C
• The AI cannot easily untangle intent vs implementation

This is how people end up saying:

"It is faster to just start over."

And they are often right — but they did not have to be.

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🔄 The Build Loop That Actually Works

Use this loop religiously:

1. Define the feature

   • What it does
   • What it explicitly does not do

2. Build only that feature

   • No future-proofing
   • No "while we are here…"

3. Verify

   • Click through it yourself
   • Try to break it
   • Make sure behavior matches intent

4. Clean obvious issues

   • Naming
   • Structure
   • Confusing logic

5. Lock it in

   • Treat it as trusted ground
   • Only then move on

Speed comes from repetition of this loop — not skipping steps.

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👀 For Non-Technical Builders

What you should watch for.

If you are not technical, here are red flags:

• You cannot explain what a feature does in simple terms
• Small changes cause unrelated things to break
• You feel afraid to touch working parts

Those are not "AI problems." They are signals that scope is too big or stability came too late.

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✅ Summary

AI is not a teammate that understands your vision.

It is a force multiplier for whatever clarity you provide.

• Clear scope → clean output
• Vague goals → creative chaos
• Stable foundations → compounding progress
• Rushed layers → exponential debt

Build less at once. Finish more deliberately. Review earlier than feels necessary.

That is how AI becomes a leverage tool instead of a liability.