Why AI Projects Fall Apart (and how to avoid it)

📌 Context

AI makes it possible to build products extremely quickly. That speed is real, and it is valuable.

But many AI built projects do not fail immediately. They slowly become harder to work with.

Changes start breaking unrelated things. Simple additions feel risky. Progress slows down even though the system technically still works.

This is rarely because the AI is bad. It usually happens because the system was never properly planned.

This document explains why planning helps AI built systems last longer, and what kind of planning actually matters.

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🧭 What We Mean by Planning

When people talk about planning with AI, they often mean different things.

In practice, there are two kinds of planning happening at the same time:

• Product level planning: deciding what should be built, what matters, and what is out of scope
• Execution planning: asking the AI to think through how it will implement changes before writing code

Both are important.

Product planning aligns intent. Execution planning reduces mistakes during implementation.

Skipping either one increases the chance the system drifts over time.

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⚠️ The Core Problem

AI does not truly understand your product or your intent.

When something is unclear, it usually does not stop and ask clarifying questions on its own. Instead, it makes assumptions and keeps going.

Those assumptions are often reasonable in isolation. Over time, they become inconsistent.

Planning reduces the number of assumptions the AI has to make before it starts building.

Fewer assumptions leads to fewer contradictions, which leads to systems that last longer.

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✍️ Step 1: Write a Clear Product Description

What to do

Write one sentence that clearly explains:

• Who this is for
• What problem it solves
• What outcome matters most

Example

Vague:

• Build a tool to manage ideas

Clear:

• This helps solo founders turn rough ideas into clear instructions they can give to AI so they can build features without constantly rewriting things later

Why this matters

Without a clear product description, the AI treats every request as a new problem.

Even if you ask the AI to plan first, it is planning against an unclear or shifting goal.

A clear description creates a stable reference point that future work can build on.

What goes wrong if you skip this

• Features that work but feel misaligned
• Inconsistent behavior across similar flows
• The AI slowly redefining what the product is

This is usually an intent problem, not a technical one.

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📦 Step 2: Define Scope Explicitly

What to do

State clearly:

• What the product should handle
• What it should not handle, at least for now

Example

Unbounded:

• Build a task management app

Bounded:

• Build a simple task list for one person
• No team features
• No permissions
• No automation
• No analytics

Why this matters

AI is optimized to be helpful.

If something seems related, it will often include it, even if you did not ask.

This is where hallucinations and overengineering usually come from.

By defining scope, you limit the problem space and focus the AI on what is actually important.

The AI generally produces better results when it has fewer degrees of freedom.

What goes wrong if you skip this

• Extra features that were never requested
• Complexity added just in case
• Systems that feel impressive but fragile

This makes future changes harder and more risky.

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🛠️ Step 3: Tell the AI How to Build

What to do

In addition to describing what to build, provide guidance on how the AI should approach the work.

Examples of guidance:

• Reuse existing logic and patterns where possible
• Avoid duplicating functionality
• Keep things simple by default
• Introduce shared helpers when behavior repeats
• Ask questions if something is unclear

If you do not understand these practices, you can ask the AI what they mean. Then include them anyway.

Example prompt snippet

• Reuse existing code and behavior
• Avoid duplication
• Keep structure consistent
• Do not introduce new abstractions unless necessary
• Ask before making assumptions

Why this matters

This is standard practice among experienced developers.

They focus on reuse, consistency, and clarity so systems remain understandable over time.

AI does not reliably follow these practices by default.

Without guidance, it often produces systems that work initially but are difficult to extend or maintain.

What goes wrong if you skip this

• Multiple versions of the same behavior
• Changes that need to be made in several places
• Fear of modifying existing functionality

This is where AI built systems often become hard to maintain.

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🎯 Step 4: Remove Ambiguity from Requirements

What to do

Make requirements specific and concise.

They do not need to be long or detailed. They just need to be unambiguous.

A useful test is to ask whether a human developer would interpret the requirement the same way you do.

Example

Ambiguous:

• Make onboarding simple

Clear:

• Onboarding should be one screen
• No account creation
• User should complete their first task in under one minute

Why this matters

AI is not great at asking clarifying questions unprompted.

When requirements are ambiguous, the AI will usually choose an interpretation and move forward.

If that interpretation is wrong, you end up rewriting work that could have been avoided.

What goes wrong if you skip this

• Features that technically match the prompt but miss the intent
• Repeated back and forth to correct behavior
• Messy internal structure that slows future work

Clarity early is almost always cheaper than cleanup later.

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🧩 The Bigger Picture

No AI built system lasts forever without maintenance.

That is expected.

The goal of planning is not to eliminate complexity. It is to slow down how quickly complexity accumulates.

With product planning and execution planning in place:

• Features layer more cleanly
• The AI remains useful for longer
• Refactors happen later and are smaller in scope

You are not preventing entropy. You are buying time.

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✅ Summary

AI does not fail because it is unintelligent.

It fails because it is forced to make too many assumptions about what you want and how to build it.

Planning reduces those assumptions.

That is what makes AI built systems more sustainable over time.