Vibe coding in practice: how a developer learned to save tokens without sacrificing code quality

A frontend developer with years of experience in B2B web services shared his journey into vibe coding — and shared specific techniques for saving tokens without sacrificing code quality.

How It All Started

The author spent most of his career designing interfaces for corporate web applications. Mobile development had attracted him for a long time, but it wasn't working out — there was no suitable project or entry point. The wave of AI tools changed the picture: modern language models can turn a few paragraphs of text description into a working mobile application in literally five minutes.

The developer decided to try vibe coding within his online service. The result was mixed: the application worked, but it didn't look the way he imagined. For a person with a design background, this was especially painful — the gap between concept and implementation was obvious at first glance.

He acknowledges the visual part as unsolved. But he did manage to solve another problem.

The Token Trap and "Spaghetti Code"

The typical vibe coding trap unfolds gradually. The first few iterations go smoothly: AI quickly generates components, functionality grows. But as the project becomes more complex, the context you need to pass to the model increases: files, dependencies, edit history, business logic. Token spending begins to grow nonlinearly. At the same time, code structure deteriorates. AI generates what was asked for, but without a systemic view of the entire project. After several iterations, different parts of the codebase begin to contradict each other — you get "spaghetti" that's hard to debug and expensive to maintain. The developer ran into this in practice and began looking for a way out without returning to completely manual code writing.

What Actually Helped

After several experiments, the author developed a set of techniques that simultaneously reduce token spending and maintain clean architecture:

• Minimal context per task — pass the model only the code related to the current change, not the entire project
• Strict decomposition — break development into small isolated steps, each formatted as a separate request
• Contract-based architecture — agree in advance with the AI about module boundaries and public interfaces between them
• Mandatory review before continuing — don't blindly accept generated code, verify 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 does well with narrow, clearly defined tasks. The more precisely the request is formulated and the less unnecessary context, the cheaper and better the result.

The Price of Speed

Vibe coding does indeed lower the barrier to entry into a new field — the author had never made mobile applications before and ended up launching a working product. But speed at the start has its price: if you don't build architecture consciously from the first steps, technical debt accumulates quickly, and its cost is measured not only in time, but in real API expenses. The designer's experience here unexpectedly helped: the habit of thinking about the system as a whole, seeing user scenarios and building hierarchy transfers to both code architecture and prompt management.

What It Means

Token savings and code quality are not contradictory goals, but interconnected consequences of the same disciplinary decisions. Vibe coding works, but requires conscious context management — and then speed stops being the enemy of structure.