Understanding Vibe Coding: The Future of AI-Assisted Programming

Introduction to AI-Assisted Programming and Vibe Coding

This article aims to unveil the mystery of “Vibe Coding” for AI product managers and tech enthusiasts. We will delve into how AI has evolved from a mere conversational language model to a tool capable of understanding complex deployment processes, bridging the gap from coding to deployment. Understanding its workings can help alleviate unnecessary anxiety and inspire more efficient utilization of these powerful tools.

What is Vibe Coding? From “Precise Instructions” to “Intuitive Understanding”

To understand Vibe Coding, we first need to recognize that it represents a fundamental shift in human-computer interaction. It signifies our transition from an era where machines are expected to “understand commands” to a new epoch where machines can “grasp intentions.”

The Essence of Vibe: Conveying Intent Rather Than Commands

The term “Vibe Coding” is inherently inspiring. Its core lies not in “Coding” but in “Vibe.” It emphasizes that what we convey to AI is a holistic feeling, an ultimate intention, and an expected user experience, rather than line-by-line syntax and logical precision. This sharply contrasts with traditional development models that demand clear, unambiguous instructions.

To illustrate, consider asking a top chef to prepare a dish:

Traditional Programming resembles giving a detailed recipe: “Take 5 grams of salt, 10 milliliters of soy sauce, preheat the oven to 180 degrees, bake for 20 minutes…” You must define every step precisely, as any mistake could lead to failure.
Vibe Coding is akin to telling the chef: “I want a dish that evokes the feeling of a Mediterranean summer evening, refreshing with a hint of lemon sweetness and the fragrance of basil.” You describe the final “Vibe,” and the chef uses their expertise to transform this abstract feeling into a delicious dish.

In Vibe Coding, AI plays the role of this “top chef.”

This shift in interaction is fundamentally from process-oriented commands to result-oriented descriptions. We can deepen our understanding through the following comparison:

Traditional Approach: Focuses on “how to do it,” requiring clear, unambiguous steps. The user is the “command issuer.”
Vibe Coding: Focuses on “what is wanted,” allowing vague, high-level natural language to describe the final goal. The user is the “vision painter.”

This represents a leap from imperative to declarative interaction—we no longer need to tell AI how to do each step; we only need to declare what we want.

Two Operational Mindsets

In practice, Vibe Coding is not monolithic; it presents two mainstream application modes based on the user’s goals and control over the code. Rather than viewing them as black-and-white choices, it is more helpful to understand them as a continuous spectrum, with each end representing different working mindsets.

“Pure” Vibe Coding (Prototype Validator Mindset): This is the most radical and exploratory end of Vibe Coding. In this mode, users fully trust AI’s output, prioritizing speed and experimentation over code rigor. Karpathy describes it as being “completely immersed in the vibe, even forgetting the existence of code.” This mode is perfect for product managers, especially for quickly validating new ideas, building “one-off weekend projects,” or developing MVPs, as the primary goal is to obtain market feedback quickly.
Responsible AI-Assisted Development (Professional Engineer Mindset): This represents the other end of the spectrum, applying Vibe Coding in professional, serious development scenarios. Here, AI is not the sole creator but a powerful “AI pair programming partner.” Developers guide AI in generating code but then conduct strict reviews and testing, ensuring they fully understand the code and ultimately bear full responsibility for product quality.

As programmer Simon Willison states, if you review and understand every line of code generated by AI, you are merely using an advanced “typing assistant,” not engaging in true “pure” Vibe Coding. This mode aims to enhance the productivity of professionals rather than replace professional judgment.

For AI product managers, understanding these two mindsets is crucial. It provides a clear decision-making framework: your work is not simply about choosing between “toys” and “production-grade systems.” When you use Vibe Coding tools to build a high-fidelity interactive prototype for handover to engineers, your actions fall in the middle of this spectrum—you seek higher fidelity and logical rigor than the “pure” mode but do not bear full responsibility for the final code in a production environment. Your position on this spectrum is entirely determined by your current goals (speed vs. robustness).

Now, let’s start from the beginning and examine the fundamental bottleneck AI initially faced.

The Initial Bottleneck: Why Large Language Models (LLMs) Were Just “Chat Machines”

To understand the value of subsequent technological solutions, we must first recognize a fundamental dilemma faced by AI programming at its inception. This dilemma stems from the core essence of large language models (LLMs).

No matter how powerful the model seems, it is essentially a “chat machine.” Its core mechanism involves receiving a text (Prompt) and generating a relevant text as a reply. It cannot actively interact with the external world, nor can it directly access or manipulate files on our local computers.

Under these limitations, the earliest AI-assisted programming experiences were extremely inefficient and cumbersome. Programmers could only play the role of “movers”:

Copy a piece of code from the local code editor.
Paste it into the AI’s chat box, along with modification instructions.
Wait for the AI to generate a reply.
Copy the AI’s returned code and paste it back into the local editor for debugging.

This repetitive “copy-paste” process severely disrupted the developer’s flow. The root of the problem lies in the fact that AI “sees” the text you send it but cannot “touch” the real files on your computer. To overcome this core bottleneck, the concept of AI Agents was born, equipping AI with the ability to perceive and manipulate the real world.

The First Leap: AI Agents, Equipping Models with “Hands” and “Feet”

The emergence of AI Agents represents a key step in AI’s evolution from “being able to speak” to “being able to act,” forming the foundation of the entire Vibe Coding technology system. It cleverly bridges the abstract language model and the concrete local environment.

By definition, AI Agents are small programs running on the developer’s local machine, serving as an “intermediate layer” between the large language model and local code. Their core operational mechanism can be broken down into three steps:

Predefined Capabilities: Developers pre-write a series of functions for the Agent to operate in the local environment. Basic capabilities include read_file (reading files), write_file (writing files), and more advanced Agents can even browse the web or execute terminal commands.
Request Packaging: When a user issues a command (e.g., “Help me fix this bug”), the Agent packages these predefined function names and usage along with the user’s instruction (Prompt) and sends them to the cloud-based large language model.
Translation and Execution: After understanding the user’s intent and the available “tools” (i.e., those functions), the large language model does not directly return code but replies with an instruction telling the local Agent: “Please call the write_file function to write the following content to a certain file.” After receiving the instruction, the Agent executes the corresponding function locally, thereby indirectly completing the read/write operations on local files.

With the ability to read and write files, the next key question became how AI could efficiently and accurately modify code. The industry explored two main approaches, one of which stood out for its efficiency and reliability.

Method One (Inefficient): Directly Generating the Modified Complete File

This method is simple and direct, but its drawbacks are evident. Even if the user only wants to modify a single character, AI must regenerate the entire file. This not only wastes computational resources (Tokens) but also poses a critical risk—when the file is long, AI struggles to ensure that while modifying the target area, it perfectly reproduces the unaltered parts, easily introducing new bugs.

Method Two (Efficient): Incremental Modifications Using “Diff Format”

This is the approach adopted by most AI programming tools today. “Diff format” is a text format that does not contain the complete file content but precisely describes: “Which line of which file needs to be replaced with what new content.” The advantages of this format include:

Long-standing, Mature Algorithms: Tools like Git and SVN have long utilized Diff algorithms, making the technology very mature.
Model Proficiency Rooted in Training Data: The model’s strong capability to generate Diff formats is not coincidental; it is rooted in its training data. The training corpus (the entire internet) is filled with vast amounts of Git commit records and version control histories, making it effectively speak a “native language” it has been deeply trained on.
Verification Mechanism Enhancing Reliability: To prevent the model from misunderstanding, the Agent performs a verification step before applying the Diff modification—checking whether the original code snippets referenced in the Diff are completely consistent with the current local file’s content. If they are inconsistent, it indicates that the model may have “misread,” prompting the Agent to abandon the modification attempt and retry. This mechanism significantly ensures the accuracy of code modifications.

Thus, with AI Agents and Diff formats, AI finally gained reliable “hands” and “feet” to modify our code. However, it soon became apparent that its “brain” seemed a bit slow, often making basic errors. Why was that?

The Upgrade in Intelligence: Context is Key to Enhancing AI’s “IQ”

After providing AI with operational capabilities, its level of “intelligence” largely depends on its depth of understanding of the current working environment. Context is the key that connects AI with the developer’s real working scene, enhancing its “IQ.”

If it relies solely on brief user inputs, AI often appears very “clumsy.” Two classic examples illustrate this:

An IDE (code editor) highlights syntax errors with red wavy lines, yet AI seems oblivious, requiring multiple attempts to correct them.
AI confidently attempts to modify a file that does not even exist in the project.

These issues stem from information asymmetry: AI cannot see the rich environmental information on the developer’s screen. Ingenious engineers realized that the solution was simple—feed AI the information visible to developers.

Therefore, modern AI programming tools actively collect and append a wealth of contextual information when sending requests to the large language model, in addition to the user’s instructions. This information typically includes:

The complete file structure tree of the current project
The filename of the file the user is currently viewing or has the cursor in
All open file tabs in the editor
The latest output from the command line (especially error messages)
Even the current time

The ultimate goal is to make the “scene” AI sees almost identical to what the user sees. By providing as much environmental information as possible, AI can more accurately understand the user’s true intentions, perceive the relationships between codes, and make smarter judgments, making the entire programming process incredibly smooth.

Having solved the intelligence problem in local coding, a larger, more complex challenge looms: how to enable AI to bridge the gap from local to cloud deployment, completing the final code deployment?

Bridging the Last Mile: From Local Code to Cloud Deployment

If AI can only generate code locally but cannot deploy it online, its value would significantly diminish, and the closed-loop experience of Vibe Coding would be unattainable. Automated deployment is the “last mile” in achieving end-to-end development automation, but the process is far more complex than local coding.

Deployment typically involves a series of tedious operations, such as configuring backend services, establishing databases, and setting up domain names. These operations exceed the traditional AI Agent’s capability to read and write local files. To enable AI to handle these complex cloud tasks, the industry has introduced two key technologies: MCPs and Engineering Templates.

MCPs: AI’s “Skill Plugin System”

You can think of MCP (Machine-Credible Plan) as the “skill plugin” or “extension store” for AI programming robots, similar to how we install extensions for browsers to enhance functionality.

MCPs allow AI programming robots to dynamically install new “skill packages,” enabling them to operate external systems they originally did not understand. For example, a cloud service provider can offer an MCP that includes skills for managing its cloud platform, such as managing databases, uploading static web pages, and creating cloud functions. When AI needs to perform these operations, it can call the interfaces provided by the MCP.

Engineering Templates: AI’s “Dedicated Instruction Manual”

MCP addresses the “how to do it” issue, but there remains the question of “what to do” and “how to write it.” Each cloud platform’s API interfaces vary widely, and new cloud services are emerging constantly, making it impossible for AI models to learn all platform implementation details in advance.

A deeper reason behind this is that after a website goes live, it is the website’s own code that continuously accesses cloud resources (like reading and writing databases), and at that moment, AI is no longer present. Therefore, AI must use the correct APIs and libraries specified by the target cloud platform when initially writing code, ensuring that the final generated code can run independently in the cloud environment.

To this end, cloud platforms typically provide a complete set of engineering templates. These templates not only include the libraries and configuration files required for the project but also contain a built-in “prompt” specifically for AI. This dedicated instruction manual clearly tells AI:

What structure the code should follow for this cloud platform.
How to call APIs to access data.
How to execute the deployment process.
Even how to consult the online documentation of the platform when encountering unknown issues.

This built-in prompt will automatically merge with the user’s instructions during the development process and be sent to the large language model, guiding it to generate code fully compatible with the specific cloud platform.

With the combination of AI Agents, rich context, MCP skill plugins, and engineering template instructions, a complete automated development and deployment process finally takes shape.

A Complete Process Review: The Entire “Vibe Coding” Experience

Now, let’s connect all the technical points discussed earlier and review a complete “Vibe Coding” user experience to establish a global understanding.

User Initiates Request: The user says in Cursor or ClaudeCode: “Help me write a website.”
Agent Collects Information: The robot (AI Agent) begins its work. It automatically collects various contextual information from the IDE (file read/write interfaces, current errors, open files, etc.) while reading the engineering template’s built-in “prompt for AI.”
Information Packaging and Sending: The Agent packages the [User Requirements] + [Template Prompt] + [Environmental Information] and sends them to the cloud-based large language model.
LLM Generates Code: The model, based on the complete information received, immediately understands that this program will be deployed on a specific cloud service. Therefore, it selects the corresponding interfaces for that platform to write the code and returns it to the local Agent in an efficient “Diff format.”
Agent Applies Modifications: Upon receiving the Diff, the Agent first verifies whether the referenced code matches the local file. If the verification passes, it applies the modifications. This process may involve repeated refinement and iteration based on code complexity until the entire website functionality is completed and successfully runs locally.
User Initiates Deployment: After testing without issues, the user issues a new command: “Deploy the website.”
Agent Calls MCP: Since the project was initially configured with the cloud service’s MCP, the Agent will now send the deployment function information provided by the MCP to the AI model. After analysis, the model returns an instruction guiding the Agent to call the corresponding MCP service.
Completing Cloud Deployment: Upon receiving the instruction, the MCP service begins operating the cloud platform, automatically completing all tasks such as establishing databases, configuring domain names, and uploading files.

Through this process, AI truly bridges the gap from idea to product. For some simple projects, it even achieves the ideal experience of “zero coding knowledge, zero deployment operations.”

Three Practical Tips for Efficient Use of Vibe Coding Tools

Understanding the above principles allows us to use AI programming tools more strategically, maximizing their effectiveness. Here are three practical tips based on technical principles.

Tip One: Clarify Your “Ultimate Destination”

Operational Advice: Before starting coding, try to choose or configure an engineering template that matches your target deployment environment. From the first instruction, clearly inform AI of the cloud platform on which your program will run.

Principle Analysis: As mentioned earlier, AI relies on the dedicated prompts and configurations in the “engineering template” to write code that adapts to specific cloud platform APIs. Clearly indicating the direction from the outset can fundamentally avoid a lot of rework due to platform incompatibility later on.

Tip Two: Create an “Information-Rich Scene” for AI

Operational Advice: When making requests to AI, keep all relevant code files open and maintain a clear project structure. If errors occur, provide the complete error messages or terminal outputs to it.

Principle Analysis: Context is key to enhancing AI’s “IQ.” The more rich and close to your real working scene you provide to AI, the more accurately it can understand your intentions and generate high-quality, relevant code.

Tip Three: Break Down Large Tasks into Smaller Steps

Operational Advice: Avoid vague and large requests like “Help me write a complete e-commerce website.” Instead, break it down into a series of specific, verifiable steps such as “Create user database table,” “Write user registration interface,” and “Implement product display page,” guiding AI to complete and test incrementally.

Principle Analysis: AI’s core workflow is a cycle of “receiving instructions → generating code (Diff) → applying modifications → validating.” Small and clear instructions align better with its working mode, significantly increasing the success rate of individual tasks and the accuracy of the code, allowing you to better control the development pace.

Conclusion: AI Replaces the “Hands,” Not the “Heart”

Returning to the initial question: “Are programmers really being replaced?”

After delving into the evolution of AI programming tools, we find that the answer is not so straightforward. We have spent considerable time teaching AI our skills, workflows, and even deployment experiences step by step. We deconstructed our abilities, packaging them into prompts and tools, aiming to liberate ourselves from the quagmire of struggling with every if-else statement.

If programs are an extension of human will, today’s AI does not push humans out of the creative process but instead returns them to their true starting point—back to being the idea-driven, creative individuals they once were, just like the programmer who first thought of letting AI write code through Agents.

It replaces our hands typing on the keyboard, not the heart that generates the first idea.