Why AI in Your Editor May Be Slowing You Down: The Case for Short Feedback Loops

AI is everywhere, and in development, it’s becoming a regular presence in our IDEs. These coding assistants promise to boost productivity by offering suggestions and even generating code at the press of a button. It sounds great—more automation, less drudgery, right? But here’s the thing: despite the convenience, integrating AI into your daily coding routine may be slowing you down in ways you don’t even realize.

The Convenience Trap

When AI first started appearing in editors like VS Code, it felt revolutionary. You could generate boilerplate, fix syntax errors, and even auto-complete complex lines of code with just a few keystrokes. It’s as if a silent collaborator were always by your side, helping you crank out features faster than ever before.

But speed isn't everything.

What if, by relying on AI to generate and correct code, we’re actually lengthening the feedback loop, making it harder for us to grasp what we’re building? What if we’re sidestepping the very process that helps us truly understand our work?

What We Lose When We Skip the Feedback Loop

In traditional software development, we often follow the mantra: red-green-refactor. Write a failing test (red), write just enough code to make the test pass (green), then clean up the code (refactor) without changing functionality. It’s a core principle, but not everyone adheres strictly to TDD. And that’s okay. The value of red-green-refactor is in the short feedback loops it creates, not in dogmatic adherence to any particular methodology.

Even if you don’t use TDD, the idea of incremental progress—building in small, testable steps—is key to writing better code. This approach forces us to understand each small step before moving on, ensuring that we're solving problems intentionally and improving along the way. AI can disrupt this incremental workflow by bypassing those critical steps.

AI in the Editor: A False Sense of Progress

When AI assistants suggest a block of code that “just works,” it’s tempting to accept it at face value. We get a dopamine hit from the perceived progress, but what happens when that suggested code doesn’t fully align with the problem we’re trying to solve?

Without diving into the details of why the code works, we end up with a false sense of progress. Sure, the feature might be completed faster, but we lose touch with the process of iteration, testing, and refactoring. We’ve skipped the understanding part.

Worse yet, AI-generated code can increase the number of bugs in the long run. Because we’re more likely to trust the AI’s suggestions without fully evaluating them, we might find ourselves debugging issues later, spending more time than if we had taken a slower, more thoughtful approach from the start.

The result? A longer feedback loop—where issues crop up further down the line—and less confidence in the codebase as a whole.

Where AI Really Shines: Early Stages of the SDLC

The truth is, AI does have a place in modern software development. But it’s not necessarily in your editor. AI tools can excel in the early stages of the Software Development Life Cycle (SDLC)—during planning, design, and prototyping phases—where the focus is on generating ideas, identifying requirements, and forecasting potential solutions.

• Planning and Brainstorming: AI can help teams analyze user data, predict project timelines, and even prioritize features based on projected impact. Tools like natural language processing can be used to scan through documentation or user feedback and extract trends that may otherwise go unnoticed. Imagine using AI to analyze thousands of feature requests and suggest which ones will have the biggest positive impact.

• Design and Architecture: AI can assist in generating initial design patterns, suggesting architectures that fit your project’s needs based on existing best practices. In large systems, AI can help visualize dependencies or even detect potential bottlenecks based on similar projects. For example, AI might analyze your microservices architecture and recommend optimizations to reduce latency or improve fault tolerance.

• Prototyping: During the early stages of a project, AI can rapidly generate prototypes, helping teams visualize solutions before investing significant resources. It can help in scaffolding an application, generating UI wireframes, or even proposing different approaches to solving the same problem, enabling quick iteration on ideas.

In these phases, AI serves as a tool to augment your decision-making, providing insight and speed in areas where human developers can become bogged down in details. But even here, the human element is vital—AI can’t make business-critical decisions or fully understand the nuances of your particular project. It’s a tool for assistance, not a replacement for thoughtful planning.

Where AI Misses the Mark in Later Stages of the SDLC

While AI can speed up the early stages of development, its effectiveness decreases as you move further into the SDLC. This is especially true during the implementation, testing, and maintenance phases, where human oversight, context, and critical thinking become irreplaceable.

• Testing: AI might help generate boilerplate test cases, but it can’t anticipate edge cases or write tests that truly reflect your application's requirements. The deeper understanding required for writing robust tests, especially in complex domains, is something only a human developer can provide.

• Refactoring and Maintenance: AI doesn’t know the future state of your application. Suggested code may work today, but it won’t necessarily align with your evolving needs. This can lead to technical debt and require more work to refactor AI-generated code later. Without that short feedback loop, these issues often go unnoticed until they become much harder to resolve.

AI Lengthens the Feedback Loop

At first glance, AI seems to shorten the feedback loop by suggesting solutions instantly. But in reality, it often lengthens it by bypassing the critical phases of understanding, testing, and refactoring.

• Understanding: AI skips the part where you deeply engage with the problem. You don’t fully learn why a solution works or doesn’t—you just accept that it does (or doesn’t).
• Testing: While AI can help write tests, it often suggests solutions that haven’t been rigorously thought through. This can lead to fragile tests that break when the code is modified.
• Refactoring: Since the AI-generated code wasn’t crafted with your particular constraints or long-term goals in mind, it can be harder to refactor down the line. This leads to technical debt and a bloated codebase.

Conclusion

AI has incredible potential to accelerate certain phases of software development—particularly during planning, design, and prototyping. But when it comes to writing code, testing, and maintaining your application, there’s no substitute for human oversight and the discipline of short feedback loops. The red-green-refactor principle isn’t just for TDD fanatics; it’s about building thoughtful, maintainable software. And AI, while useful, should be seen as a complement to that process, not a replacement.