Vibe coding is a new way of programming that shifts the developer’s role from manually writing code to collaborating with an AI assistant (powered by Large Language Models).

Instead of typing syntax line by line, you describe your intent in plain language, and the AI generates, tests, and refines the code for you. The “vibe” part comes from the fact that you’re shaping the code through conversation—going back and forth until the output matches your vision.

It was popularized in early 2025 by Andrej Karpathy, who described it as “prompting your AI to build software with you.”

You might ask. Does vibe coding uses LLMs and MCP? The short answer: Yes to LLMs, and potentially yes to MCP, depending on how it's implemented.

1. Does vibe coding use LLMs (Large Language Models)?

Absolutely. By definition, vibe coding relies on LLMs:

• The term vibe coding was coined by Andrej Karpathy in early 2025 and refers to programming through natural-language prompts rather than writing code manually—developers describe what they want, and an LLM generates it.
• Wikipedia defines vibe coding exactly like this: “the developer describes a project or task to a large language model (LLM), which generates code based on the prompt.

So yes, LLMs are the core of vibe coding.

2. Does vibe coding use MCP (Model Context Protocol)?

This depends on the platform or implementation, but MCP is absolutely used in many contexts to support vibe coding—though it's not strictly required.

• MCP is an open protocol released by Anthropic in late 2024 that standardizes how LLMs interface with external tools, data sources, or IDE environments.
• It allows LLMs to access project context (files, APIs, dependencies), execute operations, and maintain effective communication during a vibe coding session.
• Some documented installations explicitly combine vibe coding with MCP:
  • A blog post discusses “vibe coding with Copilot using the MCP Server,” where MCP enhances the AI’s integration with other tools like GitHub Copilot.
  • There are MCP server implementations—such as “Vibe Coder MCP Server”—designed to support vibe coding workflows by structuring development tasks, generating starter projects, planning, etc
  • A Google Cloud blog highlights using vibe coding with Gemini 2.5 Pro to rapidly construct MCP servers.
  • The Qdrant example shows vibe coding where MCP acts as a “semantic memory layer,” enabling an LLM (Claude Code) to search context, reuse snippets, and store results for later.

So while LLMs are mandatory for vibe coding, MCP provides the plumbing that makes context-aware, tool-integrated AI development possible.'

Vibe coding versus traditional programming

With traditional programming, you focus on the details of implementation, manually writing the specific commands, keywords, and punctuation a language requires. Vibe coding lets you focus on the desired outcome instead, describing your goal in plain language, like "create a user login form," while the AI handles the actual code.

Understanding How the Vibe Coding Process Works

Vibe coding can be thought of as working on two interconnected levels:

1. The low-level code refinement loop, where you collaborate closely with an AI assistant to shape and perfect individual code snippets.
2. The high-level application lifecycle, where you direct the AI through the bigger picture of designing, assembling, and deploying an entire system.

1. The Code-Level Workflow (Iterative Loop)

At its core, vibe coding feels like a tight feedback conversation between you and the AI. Rather than writing lines of code by hand, you’re shaping the result step by step through natural language instructions.

• Describe the goal
  You begin with a plain-language intention. For example:
  “Write a Python function that reads a CSV file and prints the first five rows.”
• AI generates code
  The assistant translates that intent into executable code. This is your “first draft.”
• Execute and observe
  You run the generated snippet. Sometimes it works perfectly; other times it exposes errors, missing logic, or inefficiencies.
• Provide feedback and refine
  Instead of manually editing the function, you describe improvements:
  “That works, but add error handling for missing files and invalid CSV format.”
• Repeat the loop
  This conversational back-and-forth continues until the snippet behaves exactly as you want. Each pass tightens the alignment between your mental model and the AI’s implementation.

👉 This loop is essentially pair programming with an LLM—but the human’s role shifts from typing syntax to giving feedback, guiding structure, and clarifying intent.

2. The Application Lifecycle (High-Level Flow)

Beyond single snippets, vibe coding also scales to building entire applications. Here, the process feels more like directing a project manager than tweaking lines of code.

• Define the system
  You start with a broad description:
  “Build a web app where users can upload images and apply AI filters.”
• Plan and scaffold
  The AI proposes a project structure—choosing frameworks, libraries, and file organization.
• Generate components
  Individual modules (backend APIs, database schemas, frontend UI) are created, often by invoking the code-level loop within each part.
• Integrate and test
  The AI helps stitch the pieces together, writes integration tests, and resolves conflicts.
• Deploy and maintain
  Finally, it suggests containerization (e.g., Docker), CI/CD setup, or deployment scripts for cloud services. With MCP or similar protocols, the AI can even interact directly with tools to automate parts of this stage.

👉 At this level, you’re less concerned with “how to implement this function” and more focused on “what kind of product or feature do I want?”

Putting It Together

• Low-level vibe coding = conversational refinement of specific code snippets.
• High-level vibe coding = orchestrating an entire application’s lifecycle through natural language prompts.

Both layers reinforce each other: you zoom in to fix details, then zoom out to guide the broader architecture. This dual process makes vibe coding feel less like “programming” and more like collaborating with a highly skilled teammate who understands both code and context.

Summary Table

Final Takeaway

• Yes, vibe coding is built on LLMs—it's how it works.
• MCP isn't mandatory, but it's often used under the hood to give those LLMs project context, external tool access, and smarter workflows.