The end of vibe coding: how to build an LLM-assisted development process that doesn’t kill the project

The term "vibe coding" has entered the lexicon of developers as an ironic description of a process in which a programmer simply feeds tasks to a language model and accepts everything it generates with minimal review. It sounds like a dream — until the project starts falling apart from the inside. One developer who has been using LLM in daily work for over a year has described this painful path in detail and, more importantly, proposed a concrete system that allows the use of AI assistants without sacrificing code quality.

The problem described by the author is familiar to almost everyone who has seriously tried to integrate ChatGPT, Claude, or other models into their workflow. At first, everything looks excellent: the model quickly generates functions, writes tests, proposes architectural solutions. The code looks clean, variables are named neatly, comments are in place.

But as the project grows, invisible technical debt accumulates. The model doesn't remember that it wrote a similar utility three chats ago. It doesn't know that the project has adopted a certain error-handling pattern.

It inserts placeholders where actual logic is needed, and does so confidently that the placeholders can easily be mistaken for working code. As a result, debugging becomes expensive, refactoring becomes painful, and trust in generated code drops.

This situation reflects a broader trend in the industry. According to various surveys, more than 70 percent of developers regularly use AI tools when writing code. However, methodologies for working with them are still forming haphazardly. Most approaches boil down to two extremes: either complete trust in the model, or complete rejection of it after the first serious bug. An intermediate, engineering-based approach is rare, and it is precisely this that the article's author attempts to formulate.

The proposed methodology is built on three principles. The first is context separation across chats. Instead of one endless dialogue in which the model gradually loses the thread, the author suggests allocating separate sessions for specific tasks: architectural decisions, business logic writing, testing, refactoring. Each chat receives its own system prompt with project context — a description of the stack, key conventions, and the current state of the module. This doesn't entirely eliminate the problem of limited context window, but it significantly reduces the likelihood that the model will "forget" critical details.

The second principle is mandatory artifacts at each step. The author requires from the model not just code, but structured output: a description of decisions made, a list of dependencies, a list of assumptions, and explicit indication of places where placeholders or simplifications are used. This transforms interaction with an LLM from a black box into a transparent process, where each architectural decision is documented and can be challenged during code review.

The third element is verification checklists. After each generation, the author runs the result through a set of checks: is there duplication with existing code, does the style match accepted conventions, are all placeholders marked as TODO, are edge cases handled correctly. Some of these checks are automated through linters and static analysis, others require manual inspection. The key idea is that verification is not an optional step, but a mandatory part of the pipeline, without which code doesn't reach the main branch.

For the industry, this approach is notable because it essentially formalizes the developer's role when working with AI assistants. A programmer ceases to be an operator who presses a button and accepts the result, and becomes an architect of the process — someone who sets the framework, controls quality, and makes final decisions. This resonates with positions increasingly voiced in large companies: AI doesn't replace an engineer, it amplifies them, but only with discipline.

It should be acknowledged that the described approach increases overhead. Context separation, writing prompts, verification — all of this requires time that could have gone into direct code writing. However, the author argues that these investments pay for themselves many times over the long run. A project that doesn't accumulate hidden technical debt ultimately develops faster than one where every sprint begins with cleaning up the consequences of thoughtless generation.

The era of naive vibe coding appears to be coming to an end. Developers who first mastered language models as a tool are now moving to the next stage — building mature processes around them. And those who manage to turn chaotic generation into a managed engineering pipeline will gain a real competitive advantage — not just speed, but speed without loss of quality.