Beyond the vibe: navigating the new world of AI-powered development

There’s a powerful temptation in modern AI software development: the siren song of the “Generate” button. With a simple prompt, we can watch an AI scaffold an entire feature, a full-stack application or a complex refactor in seconds. This is the world of “vibe coding”, where you direct an AI with a high-level idea and let it run.

For agile teams focused on rapid delivery, this feels like the ultimate shortcut. But as we embrace this new pace, we have to ask a crucial question: are we accelerating value or just accumulating a new kind of technical debt?

The answer lies in redefining our relationship with these tools. We must be the pilot, not the passenger.

The seduction of the first draft

The greatest strength of AI in coding is its ability to conquer the blank page. It’s almost always easier to critique and refine than to create from scratch. We’ve experienced this first-hand, generating a complete mobile app simply by describing the user flows, a process similar to the one outlined in how we use AI for rapid prototyping, where functional prototypes are built in just hours. The AI produced a functional prototype that would have otherwise taken days of manual effort.

This is a game-changer for the agile feedback loop. We can get tangible products into the hands of stakeholders faster, validate ideas and pivot with minimal sunk cost. In this capacity, vibe coding is an incredible catalyst. But its value ends the moment we treat that first draft as a final product.

The hidden cost of unchecked vibes

The real danger of vibe coding emerges when we blindly accept the AI’s output. The code might work, but it often becomes a “black box”, a functional but opaque system that we don’t fully understand. When the time comes to add a new feature, fix a subtle bug or scale the architecture, the initial speed advantage evaporates. You’re left trying to navigate a codebase you didn’t truly design.

We’ve learned that you can’t delegate understanding. Attempting to get an AI to fix a messy, legacy codebase is often a lesson in frustration; its output is only as good as the context you provide. If you let that same AI create a new mess from scratch, you’re just kicking the can down a much shorter road. Sustainable software requires intention.

From vibe coding to guided development: a practical approach

The path forward isn’t to reject AI but to engage with it deliberately, much like in our transition to an AI-supported delivery approach, where structure and guardrails enable both speed and quality. This is the essence of guided development, where the developer’s expertise directs the AI’s power. Here’s what that looks like in practice:

1. Be a context curator, not a hoarder: AI tools encourage us to feed them context, but more isn’t always better. Our rule is only to add specific instructions when the AI’s default behaviour is incorrect. If it generates an anti-pattern, we correct it and then save that principle as a guideline (in claude.md or similar). Flooding the context with generic advice like “write good code” is just noise. Be precise, be minimal, curate the context, don’t just dump it.

2. Leverage AI as a specialist, not a generalist: Some of our biggest wins have come from using AI to augment our own skill gaps. Imagine you are not an expert in a programming language, but you need to review a PR with it, just feed the pull request diff to a model with a sufficient context window, and get a surprisingly sharp code review with all the issues that need to be fixed. This is a perfect use case: the AI acts as a specialised consultant, but we make the final call.

3. Maintain a healthy scepticism: AI models are optimised to be agreeable. We’ve seen AI praise the “great changes” of a significant and problematic PR we asked it to review. It was only when we re-prompted with, “this is not a good PR; review it critically” that it provided the insightful and tough feedback we needed. We must remember to be the senior developer in the room, pushing back and demanding a higher standard than the default.

The future of development isn’t about handing over the keys. It’s about building a partnership where our experience, judgment and architectural vision guide the AI’s incredible ability to execute. As explored in the evolution of programming skills in the AI era, our role shifts toward high-level decision-making, architecture, and maintaining a deep understanding of blind automation.

The goal is not to vibe our way to a finished product, but to use these tools to build better and more maintainable software, faster than ever before.