Vibe coding in practice: how one developer learned to cut token costs without sacrificing code quality

A frontend developer with years of experience building B2B web services has described his path into vibe coding and shared specific techniques for saving tokens without sacrificing code quality.

How it started

The author spent most of his career designing interfaces for enterprise web applications. Mobile development had appealed to him for a long time, but it never came together: he lacked the right project or an entry point.

The wave of AI tools changed that. Modern language models can turn a few paragraphs of text description into a working mobile app in literally five minutes. The developer decided to try vibe coding for his own online service.

The result was mixed. The app worked, but it looked nothing like what he had in mind. For someone with a design background, this was especially painful: the gap between the concept and the implementation was obvious at first glance. He admits the visual side remains unsolved. The other problem, however, he did solve.

The token trap and "spaghetti code"

The typical vibe coding trap unfolds gradually. The first few iterations go smoothly: the AI generates components quickly, and the feature set grows. But as the project gets more complex, so does the context you have to feed the model: files, dependencies, edit history, business logic. Token costs start growing nonlinearly.

At the same time, the code structure degrades. The AI generates what it's asked for, but without a systemic view of the whole project. After a few iterations, different parts of the codebase start contradicting each other, and you end up with "spaghetti" that is hard to debug and expensive to maintain.

The developer hit this wall in practice and started looking for a way out that didn't mean going back to writing all the code by hand.

What actually helped

After several experiments, the author settled on a set of techniques that cut token costs and keep the architecture clean at the same time:

• Minimal context per task: pass the model only the code relevant to the current change, not the whole project
• Strict decomposition: break development into small, isolated steps, each in its own request
• Contract-based architecture: agree with the AI up front on module boundaries and the public interfaces between them
• Mandatory review before moving on: don't accept generated code blindly; check the architectural decisions before the next iteration
• Incremental sessions: short, focused prompts instead of long multi-step instructions in a single request

The key idea: AI handles narrow, well-defined tasks well. The more precisely a request is worded and the less extraneous context it carries, the cheaper and better the result.

The price of speed

Vibe coding really does lower the barrier to entering a new field. The author had never built a mobile app before and ended up shipping a working product.

But early speed comes at a price. If you don't design the architecture deliberately from the very first steps, technical debt piles up fast, and its cost is measured not only in time but in real API spending.

His design experience turned out to help here in an unexpected way. The habit of thinking about the system as a whole, seeing user flows, and building hierarchies carries over to both code architecture and prompt management.

What it means

Saving tokens and writing quality code are not competing goals; they are interconnected outcomes of the same disciplined decisions. Vibe coding works, but it requires deliberate context management, and once you have that, speed stops being the enemy of structure.