AI Behavior and Clean Coding Is Not Straightforward
We hear a lot about superior AI models for coding, often accompanied by strong recommendations regarding which one to use. Yet, after the initial build and subsequent refinements, we frequently find that while the first draft was a dream, the subsequent modular fixes have made a mess out of the original architecture. The result? Broken and bug-ridden code.
The core issue is that AI lacks a holistic architectural memory; it can fix the isolated line of code right in front of it while completely forgetting the structural integrity of the rest of the application.
Experienced programmers are careful with their workflows—tracking changes, maintaining structure, and pushing deployments as cleanly and as stable as possible. Yet, we are constantly given the impression that AI will handle all of that for you, allowing you to just chat your way to perfect code. That is simply not the case. You have to be careful and experiment with your AI models to understand their limits and remain incredibly vigilant over their code output.
Prompting Morphology Is a Skill That Often Falls Short
Most of us are in the process of learning how to write great prompts to execute good, clean, and efficient code. Yet, even with strong prompts, a great outcome is never guaranteed. Some models do a better job than others at maintaining context, but continuous testing remains key.
You may even find yourself abandoning your initial go-to AI model and switching to an alternative just to check bad code that keeps looping with every attempt to fix it. It is frustrating at first. However, as you learn the flaws and shortcomings of these models, you develop better strategies to get the clean code you originally thought your very first prompt would deliver.
Using Multiple AI Services to Ensure Good Code
If you haven’t already asked one AI model to write a highly optimized prompt for a second AI model, just give it a little time—you will. Using AI to get AI to do the best possible job is becoming common practice.
Even then, you still need to understand the mechanics of what is happening inside the code. Unfortunately, that won't be the case for most people using these tools right now. As a result, we are going to see a flood of bad code out there that barely works, riddled with security flaws and prone to breaking at any moment.
Still, good code is possible. Knowing at least a little bit about the language or framework you are using makes a massive difference. The need to be a careful caretaker of the development process isn't going away. We are often sold on the idea that you can issue a single command, walk away, and return to an immaculate product. Personally, I am convinced that stewardship skills are just as important as good prompts.
Good AI Stewardship Is…
Using AI will always demand a proactive approach; you cannot just walk away and expect good results. You need to map out the development process—even if you use AI to help you do it—and establish clear priorities, solid workflows, and a precise outline that defines the scope and goals.
These AI tools are systems that cannot be left solely to their own devices. Perhaps one day we will be able to mostly walk away, but I don’t believe there will ever be a time where we can completely detach from the engineering process. We are the stewards of these artificial systems, which will never achieve a truly closed-system status. Input from the human host will always be the key to enforcing the necessary discipline and ultimately securing a successful outcome.