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Using AI in Software Development: Why It Should Feel More Like a Train Than an Airplane
Learn why treating AI as a train with rails, rather than an airplane, leads to better software development outcomes through structured guidelines and intentional planning.
Published: November 18, 2025
Read Time: 5 min read
Status: Active
Read Time: 5 min read
Status: Active
Tags
AISoftware DevelopmentBest PracticesCode Quality
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In the last few years many developers have started using AI tools to speed up their work. These tools can write code, fix bugs and even explain whole systems in a single prompt. At first, this feels like flying in an airplane. You press a button and the output comes very fast. It looks smooth and almost magical. But there is a hidden problem with this airplane feeling.
The Problem with the Airplane Approach
When we use AI in this fast and free way, we often lose control of our own codebase. The AI may generate new files without following our patterns. It may create fresh functions instead of reusing the existing ones. It may select different naming styles each time. After a few days or weeks of this airplane style development, the project starts to shake. Parts of the system no longer fit together. You feel unsure about touching any file because you do not know what the AI has added or changed. Soon, you spend hours fixing mistakes that should not have happened. In some cases developers give up and abandon the project because it has become too messy.
Think of AI as a Train, Not an Airplane
A better way is to think of AI as a train. A train can run very fast, but only when we lay the rails first. Good rails keep the train in the right direction. They stop it from drifting into unsafe paths. They make the journey predictable and smooth. In software development these rails are the rules, patterns and guides we prepare before we ask AI to generate any code.
When we plan our rails with care, we get the best of both worlds. The AI becomes fast and helpful, but our codebase stays clean, clear and easy to maintain. We can still build features quickly, yet we do not lose the structure that makes a project long-lasting.
Building the Rails: Creating Guidelines for AI
One way to build these rails is by creating guidelines for AI. Before asking the AI to add a feature or fix something, I ask it to scan the whole project. I let it read all the frontend code, backend code, database schemas and any important files. I tell it to observe the patterns, naming rules and directory structure. Then I ask it to create a clear set of guidelines for future code generation and code refactoring.
These guidelines act as the guardrails. They tell the AI how to write new code so it fits with the existing project. They stop the AI from creating a new component when one already exists. They explain where new files should go and how variable names should be chosen. They guide the AI to write code that feels like it was written by the same team, not by a different person each time.
The Four Essential Guideline Files
In my work I usually prepare four guideline files:
1. frontend-generation.md
This file explains how new frontend features should be built. It includes folder structure, naming rules, which component libraries are used and how data moves within the app.
2. frontend-refactor.md
This file teaches the AI how to clean up existing frontend code. It covers common mistakes, patterns the project follows and how to improve readability without breaking anything.
3. backend-generation.md
This file describes how new backend routes, services or helpers should be made. It explains the structure of the codebase and the style of logic and error handling we prefer.
4. backend-refactor.md
This file gives the rules for improving old backend code, making it clearer and easier to maintain while keeping the same behavior.
The Impact of Guidelines
Whenever I ask the AI to generate new code, I tell it to read the relevant guideline file first. This simple step changes everything. The AI stops producing generic code. It starts producing code that feels like it belongs to my project. The code becomes more predictable, and the overall project stays organized.
Planning these rails also helps me as a developer. When I return to the project after weeks or months, I know the code is still in a shape I understand. The guidelines become a shared understanding between me and the AI. They protect the codebase from turning into a patchwork of mismatched ideas.
Why Developers Skip This Step (And Why They Shouldn’t)
Many developers skip this step because it feels slow in the beginning. But the time saved later is far more. You avoid confusion, rework and frustration. You avoid the shaky foundation that comes from the airplane approach. And you gain the steady and reliable progress that comes from the train approach.
Conclusion: Finding the Balance
AI can speed up software development in a huge way, but it works best when we guide it with intention. If we lay strong rails through planning, rules and clear guidelines, the AI becomes a powerful tool. It helps us move fast without losing clarity. It helps us write more code without creating more problems. It helps us stay in control of our own projects.
This is the balance every developer needs today. We do not give all control to the AI, and we do not ignore it either. We simply build the rails, set the direction and let the train run smoothly.
