Vibe Coding：AI驱动编程新范式，从写代码到引导AI的变革

引言

软件开发领域正经历一场由生成式人工智能驱动的深刻变革。一种名为“Vibe Coding”（氛围编码）的新兴范式，正将编程的核心从精确的语法指令转向高层次的意图表达。这一理念由 OpenAI 联合创始人 Andrej Karpathy 于 2025 年初明确提出，其核心主张是“忘记代码的存在，专注于想法的实现”。Vibe Coding 标志着编程活动的一次根本性转变：开发者不再仅仅是代码的撰写者，而是转变为产品思维的引导者与 AI 的协作者，通过自然语言与大型语言模型交互，共同将创意转化为可运行的软件。

软件开发领域正经历一场由生成式人工智能驱动的深刻变革。一种名为“Vibe Coding”（氛围编码）的新兴范式，正将编程的核心从精确的语法指令转向高层次的意图表达。这一理念由 OpenAI 联合创始人 Andrej Karpathy 于 2025 年初明确提出，其核心主张是“忘记代码的存在，专注于想法的实现”。Vibe Coding 标志着编程活动的一次根本性转变：开发者不再仅仅是代码的撰写者，而是转变为产品思维的引导者与 AI 的协作者，通过自然语言与大型语言模型交互，共同将创意转化为可运行的软件。

什么是 Vibe Coding？

Vibe Coding 是一种以自然语言为驱动、AI 为核心的编程新范式。它彻底改变了传统编程中开发者与机器交互的方式。开发者通过用自然语言描述功能需求、业务逻辑或系统架构，由大型语言模型（如 GPT、Claude 等）理解意图并生成相应的代码。开发者在此过程中的主要职责是清晰定义问题、审查 AI 输出、提供反馈并进行迭代优化。这种方式将编程从命令式、语法细节繁重的活动，提升至语义级、以目标为导向的表达。

Vibe Coding 是一种以自然语言为驱动、AI 为核心的编程新范式。它彻底改变了传统编程中开发者与机器交互的方式。开发者通过用自然语言描述功能需求、业务逻辑或系统架构，由大型语言模型（如 GPT、Claude 等）理解意图并生成相应的代码。开发者在此过程中的主要职责是清晰定义问题、审查 AI 输出、提供反馈并进行迭代优化。这种方式将编程从命令式、语法细节繁重的活动，提升至语义级、以目标为导向的表达。

Karpathy 将这种方式描述为“完全沉浸于编程的‘氛围’中”，甚至忘记代码本身的存在。与传统编程相比，Vibe Coding 更注重“想要实现什么”，而非“如何用特定语法实现”。其核心特点包括：

意图驱动：关注目标而非实现路径。Focus on the “what” rather than the “how”.
AI 协作为中心：LLM 是主要的代码生成引擎。LLMs serve as the primary code generation engine.
快速迭代：接受 AI 生成的代码作为起点，通过快速反馈循环进行优化。Accept AI-generated code as a starting point and refine through rapid feedback loops.
角色转变：开发者成为“引导者”和“审查者”。Developers become “orchestrators” and “reviewers”.

核心概念与演进

编程范式的演进

软件开发中 AI 的融入程度可以划分为几个清晰的阶段：

传统阶段：开发者独立完成从设计、编码到测试的全过程，对 AI 的信任度极低，AI 仅作为信息查询工具。The traditional stage: Developers independently complete the entire process from design and coding to testing, with minimal trust in AI, which is used only as an information lookup tool.
AI 辅助阶段：AI 工具（如智能补全、代码建议）开始集成到 IDE 中，辅助开发者完成大部分编码工作，但核心逻辑和架构仍需开发者深度验证。效率得到提升。The AI-assisted stage: AI tools (e.g., intelligent completion, code suggestions) are integrated into IDEs, assisting developers with most coding tasks, but core logic and architecture still require deep validation by developers. Efficiency improves.
Vibe Coding 阶段：开发者将绝大部分原始代码的实现交给 AI，自身角色转变为需求澄清者、任务分解者、质量审查者和系统整合者。开发的核心变为“引导”而非“编写”。The Vibe Coding stage: Developers delegate the majority of raw code implementation to AI, transforming their role into that of requirement clarifiers, task decomposers, quality reviewers, and system integrators. The core of development becomes “orchestration” rather than “writing”.

如今，完全不使用 AI 辅助工具的纯手工编码时代已渐行渐远。根据使用模式，开发者大致可分为两类：

Bootstrappers（启动者）：从零开始构建项目 MVP，利用 AI 在几小时或几天内生成完整的初始代码库，专注于快速验证想法和获取早期用户反馈。例如，快速将一种语言的 SDK 移植到另一种语言，或构建一个简单的 App 原型验证功能可行性。Bootstrappers: Build project MVPs from scratch, using AI to generate a complete initial codebase within hours or days, focusing on rapid idea validation and early user feedback. For example, quickly porting an SDK from one language to another, or building a simple app prototype to test feature feasibility.
Iterators（迭代者）：在日常开发工作流中使用 Cursor、Windsurf 等 AI 编程工具。他们利用 AI 完成代码编写、复杂重构、测试生成和文档编写，将 AI 视为“结对编程”伙伴。AI 加速了他们的实现速度，但其专业知识确保了代码的可维护性，并在核心链路上保持绝对掌控。Iterators: Use AI programming tools like Cursor or Windsurf in their daily development workflow. They leverage AI for coding, complex refactoring, test generation, and documentation, treating AI as a “pair programming” partner. AI accelerates their implementation, but their expertise ensures code maintainability and absolute control over core components.

Vibe Coding 的典型工作流

当前主流的 Vibe Coding 平台通常围绕一个四步核心循环构建：

提出需求：用自然语言清晰描述目标，例如：“创建一个显示全球主要城市实时天气的仪表盘网页。” Define Requirements: Clearly describe the goal in natural language, e.g., “Create a dashboard webpage that displays real-time weather for major global cities.”
生成代码：AI 根据需求描述，生成相应的前端代码、后端逻辑、数据库 schema 或配置脚本。Generate Code: The AI generates corresponding front-end code, back-end logic, database schemas, or configuration scripts based on the requirement description.
审查与反馈：开发者评估生成的代码和输出结果，提出修改、优化或增强建议，例如：“将温度单位改为摄氏度，并添加未来24小时预报图表。”Review and Feedback: The developer evaluates the generated code and output, suggesting modifications, optimizations, or enhancements, e.g., “Change the temperature unit to Celsius and add a 24-hour forecast chart.”
集成与发布：在功能满足要求后，将代码集成到项目中，完成测试、构建并部署到生产环境。Integrate and Release: Once the functionality meets requirements, integrate the code into the project, complete testing, build, and deploy to production.

系统功能分层

一个完整的 Vibe Coding 系统可以抽象为以下五层：

用户输入层：接收并预处理用户的自然语言指令，管理对话上下文和约束条件。输入质量直接决定最终输出准确性。Input Layer: Receives and preprocesses user’s natural language instructions, managing conversation context and constraints. Input quality directly determines final output accuracy.
处理层：将自然语言转化为结构化指令，涉及需求分析、任务分解和提示词工程。这是开发者可以重点学习和提升的环节。Processing Layer: Transforms natural language into structured instructions, involving requirement analysis, task decomposition, and prompt engineering. This is a key area for developer learning and improvement.
核心引擎：集成了大语言模型、知识库、规划模块等，是执行代码生成和智能决策的“大脑”。Core Engine: Integrates LLMs, knowledge bases, planning modules, etc., serving as the “brain” for code generation and intelligent decision-making.
质量保证层：对生成的代码进行语法检查、逻辑验证、安全扫描、性能分析和测试生成，确保代码健壮性。Quality Assurance Layer: Performs syntax checking, logic validation, security scanning, performance analysis, and test generation on the generated code to ensure robustness.
输出与集成层：以友好方式呈现代码，并提供与版本控制、CI/CD 管道等开发基础设施集成的能力。Output and Integration Layer: Presents code in a user-friendly manner and provides integration capabilities with development infrastructure like version control and CI/CD pipelines.

驱动协议：从人机协作到智能体生态

2025年以来，一系列新协议的出现，正推动 Vibe Coding 从“人机对话”向“多智能体协同”的生态系统演进。

MCP：标准化了模型与外部数据源、工具之间的连接，让 LLM 能通过自然语言轻松调用各种能力，无需关心底层集成细节，极大提升了开发的灵活性和上下文感知能力。Model Context Protocol (MCP): Standardizes the connection between models and external data sources/tools, allowing LLMs to easily invoke various capabilities via natural language without worrying about underlying integration details, greatly enhancing development flexibility and context awareness.
A2A：实现了不同 AI 智能体之间的直接通信与协作，使得由多个专业化智能体（如架构师、前端、后端、测试智能体）组成的“AI 团队”成为可能，标志着软件生产向更高程度的自动化迈进。Agent-to-Agent (A2A) Protocol: Enables direct communication and collaboration between different AI agents, making it possible to form “AI teams” composed of specialized agents (e.g., architect, front-end, back-end, testing agents), signaling a move towards higher levels of automation in software production.
AG-UI：定义了智能体与用户界面（前端应用）之间的标准化流式交互协议，补充了智能体与人类用户交互的最后一环。Agent-User Interaction (AG-UI) Protocol: Defines a standardized streaming interaction protocol between agents and user interfaces (front-end applications), completing the final link in agent-human user interaction.

这三个协议相辅相成，共同构建了 用户 – 智能体 – 大模型 – 工具 之间完整的标准化交互体系，为复杂、自动化的 Vibe Coding 工作流奠定了基础设施。

Vibe Coding 的优势与价值

降低编程门槛

Vibe Coding 使不具备传统编程技能的人也能将创意转化为软件。过去需要数月学习才能开始的项目，现在通过自然语言描述，可能在几小时内就能看到可运行的原型。这极大地促进了技术的民主化，让产品经理、设计师、领域专家等都能直接参与功能实现。

Vibe Coding enables individuals without traditional programming skills to turn ideas into software. Projects that previously required months of learning can now see runnable prototypes within hours through natural language description. This greatly democratizes technology, allowing product managers, designers, domain experts, and others to directly participate in feature implementation.

提升开发效率

对于专业开发者，Vibe Coding 能自动化处理样板代码、数据转换、简单 CRUD 接口、文档编写等重复性任务。开发者得以将精力集中于系统架构设计、复杂业务逻辑实现、性能优化和创造性解决问题上，整体开发效率显著提升。

For professional developers, Vibe Coding automates repetitive tasks such as boilerplate code, data transformation, simple CRUD interfaces, and documentation writing. Developers can then focus their energy on system architecture design, complex business logic implementation, performance optimization, and creative problem-solving, significantly improving overall development efficiency.

促进创新与快速验证

低门槛和高速迭代的特性，使得探索新想法、验证产品假设的成本急剧下降。团队可以快速构建多个原型进行 A/B 测试，或个人可以轻松开发浏览器插件、小程序等“微产品”来验证市场需求，从而激发更广泛的创新。

The low barrier to entry and high-speed iteration drastically reduce the cost of exploring new ideas and validating product hypotheses. Teams can quickly build multiple prototypes for A/B testing, or individuals can easily develop “micro-products” like browser extensions or mini-programs to validate market demand, thereby stimulating broader innovation.

(Due to length constraints, the following sections will be summarized. The full article would continue with detailed discussions on “Tools and Collaboration Models,” “Challenges and Controversies,” “The Evolution of the Developer Role,” “Best Practices and Learning Recommendations,” and “Future Trends.”)

挑战与应对策略

尽管前景广阔，Vibe Coding 也面临严峻挑战，主要集中在技术风险和认知危机两方面。

技术风险包括：AI 可能生成看似可用但结构混乱、可维护性差的代码；代码中可能隐藏安全漏洞（如未过滤的 SQL 查询）；对过时或具有传染性许可证的第三方库的依赖；以及忽视性能瓶颈等。

Technical risks include: AI may generate seemingly functional but poorly structured, hard-to-maintain code; code may hide security vulnerabilities (e.g., unfiltered SQL queries); dependencies on outdated or copyleft-licensed third-party libraries; and neglect of performance bottlenecks.

认知危机体现在：过度依赖可能导致底层调试和深度理解能力的退化；开发者可能盲目信任 AI 输出（“幻觉信任”）；责任归属变得模糊；初级开发者面临身份焦虑和技能发展路径的挑战。

The cognitive crisis manifests as: Over-reliance may lead to degradation of low-level debugging and deep understanding skills; developers may blindly trust AI output (“hallucination trust”); accountability becomes blurred; and junior developers face challenges with identity anxiety and skill development paths.

应对这些挑战，需要建立严格的混合工作流和质量门禁：

明确分工：AI 负责草稿、样板代码和简单任务；人类牢牢掌控架构、核心逻辑、安全组件和最终审查。Define clear division of labor: AI handles drafts, boilerplate code, and simple tasks; humans firmly control architecture, core logic, security components, and final review.
严格执行审查：所有 AI 生成的代码必须经过人工审查，重点关注逻辑正确性、安全性、可维护性和规范遵循。Implement strict review processes: All AI-generated code must undergo human review, focusing on logical correctness, security, maintainability, and adherence to standards.
倡导深度理解：鼓励开发者解释和重构 AI 生成的代码，保持对代码库的掌控力，避免成为“黑盒”用户。Advocate for deep understanding: Encourage developers to explain and refactor AI-generated code, maintaining control over the codebase and avoiding becoming “black-box” users.

总结

Vibe Coding 远不止是一种新工具，它代表着软件工程范式的一次根本性跃迁。软件开发的核心正在从“工程师编写指令”转向“人类与 AI 协同创造”。在这一过程中，开发者的角色进化为更具战略性的问题定义者、意图引导者和系统调优者。

Vibe Coding is far more than a new tool; it represents a fundamental shift in software engineering paradigms. The core of software development is moving from “engineers writing instructions” to “humans and AI co-creating.” In this process, the developer’s role evolves into a more strategic problem definer, intent orchestrator, and system optimizer.

未来的成功将取决于我们能否实现平衡的融合：充分利用 AI 带来的前所未有的速度和自动化能力，同时坚守人类在复杂决策、架构设计、质量把控和伦理责任方面的不可替代价值。Vibe Coding 不是程序的终结，而是创意表达新时代的开始。

Future success will depend on our ability to achieve a balanced fusion: fully leveraging the unprecedented speed and automation AI offers, while steadfastly upholding the irreplaceable human value in complex decision-making, architectural design, quality control, and ethical responsibility. Vibe Coding is not the end of programming, but the beginning of a new era of creative expression.

参考资料:

Andrej Karpathy on Vibe Coding
Beyond Vibe Coding (O‘Reilly)
Anthropic: Vibe Coding in Production
Lovable.dev: What is Vibe Coding?
MCP, A2A, and AG-UI Protocol Specifications