The End of Vibe Coding: How to Build an LLM Development Workflow That Won't Wreck Your Project

According to various surveys, more than 70% of developers already use AI tools regularly when writing code — yet the methods for working with them are still taking shape ad hoc. One developer who has used LLMs in his daily work for more than a year described the painful journey from naive "vibe coding" to an engineering system built on three principles, one that lets him use AI assistants without degrading the quality of the codebase.

What vibe coding is and why it fails

Developers coined "vibe coding" as an ironic label for a workflow in which a programmer simply feeds tasks to a language model and accepts whatever it generates, with almost no review. At first, everything looks great: the model quickly writes functions and tests and suggests architectural decisions. The code looks clean, the variables are neatly named, and the 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. It doesn't know about the project's established error-handling pattern.

And it drops in stubs where real logic belongs — so confidently that the stubs are easy to mistake for working code. As a result, debugging gets more expensive, refactoring gets more painful, and trust in generated code erodes. Most approaches to working with LLMs today come down to two extremes: either trust the model completely or abandon it entirely after the first serious bug.

The methodology's three principles

The first principle is splitting context across chats. Instead of one endless conversation in which the model gradually loses the thread, the author sets up separate sessions for specific tasks: architectural decisions, business logic, testing, and refactoring. Each chat gets its own system prompt describing the stack, the key conventions, and the current state of the module. This doesn't fully solve the problem of a limited context window, but it reduces the risk that the model will "forget" critical details.

The second principle is mandatory artifacts at every step: not just code, but structured output that describes the decisions made, lists the dependencies and assumptions, and explicitly flags every place where stubs or simplifications were used. This turns working with an LLM from a black box into a transparent process in which 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: Does it duplicate existing code? Does the style match the project's conventions? Are all stubs marked as TODO? Are edge cases handled correctly? Linters and static analysis automate some of these checks, while others require manual inspection — but verification isn't optional. It's a mandatory part of the pipeline, and without it, code doesn't make it into the main branch.

The cost of discipline and the developer's role

This approach adds overhead: splitting context, writing prompts, and verifying output all take time that could otherwise go into writing code directly. The author argues that the investment pays for itself many times over in the long run. A project that doesn't accumulate hidden technical debt ultimately moves faster than one where every sprint starts with cleaning up the fallout from mindless generation.

For the industry, the approach stands out 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 calls. This echoes a position that large companies increasingly voice: AI doesn't replace engineers, it amplifies them, but only when there's discipline. The era of naive vibe coding appears to be ending. 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 sacrificing quality.

What is vibe coding?

Vibe coding is an ironic term for the practice of simply feeding tasks to a language model and accepting nearly everything it generates without serious review. At first, the code looks clean and tidy, but as the project grows, hidden technical debt builds up: duplicated logic, context the model has forgotten, and stubs that are easy to mistake for working code.

What three principles does the author's methodology propose?

The methodology rests on three principles: splitting context into separate chats for specific tasks (architecture, business logic, testing, refactoring), each with its own system prompt; mandatory structured artifacts at every step — descriptions of decisions, dependencies, assumptions, and explicitly flagged stubs; and verification checklists after each generation that check for duplication, style, TODO markers, and edge cases, with some of these checks automated by linters.

How many developers already use AI tools in their work?

According to various surveys, more than 70% of developers regularly use AI tools when writing code. Yet, as the author notes, a middle-ground engineering approach between fully trusting the model and abandoning it entirely after the first serious bug remains rare.