Vibe Coding in Practice: The Four Core Competency Pillars for Delivering Projects with AI Programming

Why You Still Can't Deliver Projects After a Year of Learning AI Programming

Many developers share the same frustration: they've spent an entire year watching AI programming tutorials, tried over a dozen tools, and built dozens of demos—yet when it comes to actually delivering a production-ready project, nothing holds up. Where exactly is the problem?

According to analysis from related tutorials on Bilibili, the answer is blunt—you've been learning nothing but fragments. You think you're learning AI programming, but you're really just collecting tools and memorizing buttons. This fragmented approach to learning can never be assembled into a complete capability set for stable project delivery. From a cognitive science perspective, fragmented learning fails to produce delivery capability because isolated knowledge points lack contextual connections. They can only remain at the level of "declarative knowledge" (knowing what something is) without forming "procedural knowledge" (knowing how to do it). True project delivery requires a complete mental model—a systematic cognitive framework that enables correct decision-making when facing complex scenarios.

Vibe Coding isn't some new tool—it's considered the new paradigm that will define how programmers work over the next five years. The concept was first introduced by Andrej Karpathy in early 2025—the former Tesla AI Director and OpenAI co-founder described a fundamentally new way of programming on social media: developers no longer write code line by line but instead describe their intent in natural language, let AI generate the code, and simply verify whether the final result meets expectations. This approach transforms programming from "precisely controlling every line of code" to "describing goals and validating output"—essentially a fundamental shift in human-machine collaboration.

Vibe Coding encompasses mainstream AI programming environments including Codex, Claude Code, and Cursor. Codex is OpenAI's asynchronous code agent that can independently complete coding tasks in the cloud. Claude Code is Anthropic's command-line AI programming assistant that runs directly in the terminal and excels at understanding large codebases. Cursor is an AI-enhanced IDE based on VS Code that deeply integrates AI capabilities into the editor workflow. Each has its own focus, but all point in the same direction—letting AI handle more coding work while humans focus on decisions and verification. The core of Vibe Coding lies not in the tools themselves, but in a complete methodology system spanning from cognition to engineering.

Module One: Paradigm Cognitive Reconstruction—From Reading Code to Evaluating Results

The most fundamental dividing line between traditional software engineering and Vibe Coding lies in how you verify AI-generated code.

Stop Reading Code Line by Line

When AI finishes writing a large block of code, many people's instinct is to review it line by line. But under the Vibe Coding paradigm, this is precisely the biggest mistake. The correct approach is: check whether the UI and functionality are correct—if it runs and the results are right, move on.

There's a profound engineering logic behind this shift: the core purpose of traditional code review is to ensure code quality and maintainability. But when AI can regenerate code from requirements at any time, the very definition of "maintainability" changes. Code is no longer an asset that humans must maintain long-term—it's more like an intermediate artifact for achieving functional goals. The focus of verification thus shifts from "is the code elegant" to "is the functionality correct."

This tutorial emphasizes that approximately 88% of programmers stumble at this cognitive shift from the very beginning. They can't accept not reviewing code line by line, and end up getting bogged down in details, making them less efficient than before. Cognitive reconstruction is the first threshold to mastering AI programming, and also the most easily overlooked.

Module Two: Open Source Ecosystem Adaptation—From Building from Scratch to Standing on Giants' Shoulders

The second capability leap is shifting from "starting from zero" to "leveraging existing projects for rapid delivery."

Adapting Existing Projects Is Ten Times Faster Than Building from Scratch

For projects that need to go live, building from scratch is often unrealistic. GitHub has a wealth of high-quality open source projects that can be adapted ten times faster. But here's the critical mindset reversal: the approach to secondary development is completely opposite to building from scratch.

When building from scratch, you can let AI start writing directly. But when doing secondary development, you must first let AI thoroughly understand the entire project before it changes a single line of code. Otherwise, AI might completely disfigure the entire architecture within minutes. This principle of "understand first, modify later" is the core guarantee for successful open source adaptation.

In practice, this means you need AI to first analyze the project's directory structure, dependency relationships between core modules, data flow patterns, and existing design patterns—forming a holistic understanding of the project before making targeted local modifications. This is similar to how traditional developers approach unfamiliar projects, but with AI's code comprehension capabilities, this process can be shortened from days to hours. The key is whether you know how to guide AI to "read" before "writing."

Module Three: SDD (Spec-Driven Development)—From Handwritten Prompts to Structured Specifications

When project scale reaches a certain level, the approach of handwriting prompts completely breaks down.

The Chain Reaction of Prompt Loss of Control

Once a project scales up, you change one function and AI might simultaneously modify three other related functions. When you check GitHub, the scope of changes has spiraled out of control, making the code unmaintainable.

This is exactly the problem that SDD (Spec-Driven Development) solves. SDD didn't appear out of nowhere—it inherits ideas from several classic software engineering methodologies: TDD (Test-Driven Development) emphasizes writing tests before code, BDD (Behavior-Driven Development) emphasizes describing requirements in business language, and SDD is their evolution for the AI era—using structured specification documents to constrain AI's code generation behavior. The core problem it solves is: when AI has powerful code generation capabilities, how do you ensure its output conforms to the project's overall architectural design and quality standards?

By using standardized documents to drive development, you can constrain AI's behavior within controllable bounds. There are currently three mainstream frameworks for developers to choose based on their scenarios:

• Open Spec: A lightweight entry-level solution suitable for quick onboarding. It provides minimal document templates that let developers experience document-driven workflows at the lowest cost
• Spec Kit: A complete scaffolding that covers scaled project requirements. It includes a full documentation system spanning requirements definition, architectural design, and interface specifications—suitable for team collaboration scenarios
• Superpowers: A behavioral constraint framework that strengthens control over AI output. It focuses on defining AI's behavioral boundaries in different scenarios to ensure generated code doesn't overstep

Shifting from handwritten prompts to document-driven development essentially upgrades random, ad-hoc instructions into structured, reusable engineering specifications. It's like upgrading from verbal requirement communication to formal Product Requirements Documents (PRDs)—more upfront investment, but in return you get predictable, reproducible delivery quality.

Module Four: Rule Constraints and Project Constitution—Making AI Work Within Rules

The final module is making AI work within established rules, rather than letting it run free.

Establishing a "Constitution" for Your Project

The specific approach is: place an MD rules document in the project root directory. In Claude Code, it's CLAUDE.md; in Cursor, it's .cursorrules. This document serves as the project's "constitution," defining the behavioral boundaries AI must adhere to.

These project rule files are essentially a form of "system-level prompt engineering." Unlike prompts written ad-hoc during each conversation, these files are automatically loaded into context by AI tools during every interaction, forming persistent behavioral constraints. This is similar to .editorconfig or .eslintrc configuration files in traditional software engineering, but the scope of constraints extends from code formatting to AI's behavioral patterns, code style, architectural decisions, and higher-level concerns. For example, you can specify in the rules file: "Do not modify any files under the /core directory," "All new functions must include type annotations," "Database operations must go through the ORM layer—direct SQL is prohibited," and so on.

Going further, you can set up "checkpoints" at all critical nodes. This way, even if AI wants to proceed autonomously, it must stop and wait for your confirmation before continuing. These human-intervention checkpoints are the entry ticket to stable project delivery. This "Human-in-the-Loop" design philosophy is widely advocated in AI safety and reliability research—it leverages AI's productivity advantages while preserving human ultimate control over critical decisions.

From "Able to Write Code with AI" to "Able to Deliver Projects with AI"

Connecting these four modules together completes a qualitative leap: from "being able to write code with AI" to "being able to deliver projects with AI."

This is precisely the fundamental difference between systematic learning and fragmented tutorials:

Fragments teach you 100 tools; a system teaches you one winning playbook.

For developers who want to truly establish themselves in the AI programming era, mastering scattered tool techniques is far from enough. True competitive advantage comes from building a complete delivery capability chain of "cognitive reconstruction—open source adaptation—document-driven development—rule constraints." These four modules aren't simply parallel—they're progressively layered: cognitive reconstruction lets you accept the new paradigm, open source adaptation lets you start quickly, document-driven development lets you manage scaled complexity, and rule constraints ensure delivery quality.

Whether you're using Codex, Claude Code, or Cursor, tools will continually evolve, but the value of this Vibe Coding methodology system will persist. This is perhaps the real reason Vibe Coding is called "the new paradigm for the next five years"—it doesn't depend on any specific tool's features, but rather establishes a collaborative framework that adapts to AI's continuously evolving capabilities.