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

More than 70% of developers, according to various surveys, already use AI tools regularly when writing code — but methodologies for working with them are still forming spontaneously. One developer, who has used LLMs in his day-to-day work for more than a year, described a painful journey from naive "vibe coding" to an engineering system built on three principles that allows AI assistants to be used without sacrificing the quality of the codebase.

What is vibe coding and why it falls short

The term "vibe coding" entered developer parlance as an ironic description of a process in which a programmer simply feeds tasks to a language model and accepts almost everything it generates, with little to no review. At first everything looks great: the model quickly generates functions, writes tests, proposes architectural decisions, the code looks visually clean, variables are named neatly, and comments are in place. But as the project grows, invisible technical debt accumulates.

The model doesn't remember that it already wrote a similar utility three chats ago, doesn't know about the error-handling pattern adopted in the project, and inserts stubs where real logic is needed — and it does so with such confidence that the stubs are easily mistaken for working code. As a result, debugging becomes more expensive, refactoring more painful, and trust in the generated code declines. Most approaches to working with LLMs today boil down to two extremes: either complete trust in the model, or a total rejection of it after the first serious bug.

Three principles of the methodology

The first principle is separating context by chats. Instead of one endless dialogue in which the model gradually loses the thread, the author sets up separate sessions for specific tasks: architectural decisions, writing business logic, testing, refactoring. Each chat gets its own system prompt describing the stack, key conventions, and the current state of the module — this doesn't eliminate the problem of a limited context window entirely, but it reduces the risk of the model "forgetting" critically important details.

The second principle is mandatory artifacts at every step: not just code, but a structured output describing the decisions made, a list of dependencies, a list of assumptions, and an explicit indication of places where stubs or simplifications were used. This turns working with an LLM from a black box into a transparent process, where every decision is documented and can be challenged during code review.

The third element is verification checklists. After each generation, the author checks the result: whether there is duplication with existing code, whether the style matches accepted conventions, whether all stubs are marked as TODO, and whether edge cases are handled correctly. Some of the checks are automated with linters and static analysis, while others require manual inspection — but verification isn't optional; it's a mandatory part of the pipeline without which the code doesn't make it into the main branch.

The cost of discipline and the developer's role

The approach described increases overhead: separating context, writing prompts, and verification all take time that could otherwise go into writing code directly. The author argues that these investments pay off many times over in the long run — a project that doesn't accumulate hidden technical debt ultimately develops faster than one where every sprint begins by cleaning up the consequences of mindless generation.

For the industry, the approach is notable because it formalizes the developer's role when working with AI assistants: the programmer stops being an operator who presses a button and accepts the result, and becomes the architect of the process — the one who sets the boundaries, controls quality, and makes the final decisions. This echoes a position increasingly voiced at large companies: AI doesn't replace the engineer, it augments them, but only when discipline is in place. The era of naive vibe coding, it seems, is coming to an end — developers are moving toward building mature processes around language models, and those who turn chaotic generation into a managed engineering pipeline will gain a real competitive advantage: not just speed, but speed without loss of quality.

What is vibe coding?

Vibe coding is an ironic term for the practice in which a programmer simply feeds tasks to a language model and accepts almost everything it generates, without serious review. At first the code looks clean and tidy, but as the project grows, hidden technical debt accumulates: duplicated logic, context the model has forgotten, and stubs that are easily mistaken for working code.

What three principles does the author of the methodology propose?

The methodology is built on three principles: separating context into distinct chats for specific tasks (architecture, business logic, testing, refactoring), each with its own system prompt; mandatory structured artifacts at every step — describing decisions, dependencies, assumptions, and explicit stubs; and verification checklists after each generation that check for duplication, style, TODO markers, and edge cases, some of which are automated with linters.

How many developers already use AI tools at work?

According to various surveys, more than 70% of developers regularly use AI tools when writing code. At the same time, as the author notes, an intermediate engineering approach between complete trust in the model and total rejection of it after the first serious bug still remains rare.