A Real-World Review of the AI-Native IDE Cursor: Setup, Strengths, and Clear Limitations

17 Jun 2026

Countless AI coding assistants have emerged. GitHub Copilot has evolved beyond basic autocompletion to become an integral part of the development workflow, and various IDEs are rapidly incorporating their own AI features. However, most of these remain as plugins added to existing editors.

Cursor takes a slightly different approach. While based on VS Code, it markets itself as an “AI-native” IDE, designed from the ground up with AI interaction at its core. It goes beyond simple code completion to offer an experience of building software by understanding the context of the entire codebase and conversing with the user. In this article, I’ll share my experience adopting Cursor for professional work, from the setup process and impressive features to the clear limitations I encountered.

What Makes Cursor Different?

Cursor’s biggest differentiator is that AI is not an add-on but a core component of the editor. Since it’s a fork of VS Code, you can use all your existing VS Code extensions and settings, lowering the barrier to entry.

Its core functionality is summarized by a shortcut-based interface:

Cmd+K (Generate/Edit): This is its most powerful feature. By selecting a block of code and pressing Cmd+K, you can directly modify or generate code using natural language commands like “Convert this code to TypeScript” or “Add exception handling logic here.”
Cmd+L (Chat): A chat panel opens in the sidebar, allowing you to ask questions based on the context of the currently open file or the entire codebase.
@ Symbol for Context Referencing: In the chat panel, you can explicitly specify the context for the AI to reference by typing @Codebase, @File, @Terminal, etc. This plays a crucial role in reducing “hallucinations,” where the AI produces nonsensical answers.

These features work together not just to write code for you, but to understand your intent and help you maintain consistency across the entire codebase.

Initial Setup and Essential Configuration

Installation is simple: just download it from the official website. On the first launch, it asks if you want to import your existing VS Code settings, which makes migration painless.

The most important step is to configure the API key for the LLM you want to use.

Open the command palette with Cmd+Shift+P and search for Cursor: Configure AI.
In the Models section, you can select your preferred model (e.g., GPT-4o, Claude 3 Opus).
Enable the Bring Your Own Key option and enter the API key for your service. For example, if you’re using an OpenAI key, you would paste <YOUR_OPENAI_API_KEY> into the field.

Using your own key allows you to access Pro features without subscribing to Cursor’s paid plan, and you only pay for what you use, which can be more economical. The wide selection of models is another significant advantage.

Use Cases in a Real-World Codebase

Let’s look at Cursor’s utility through practical examples rather than just theoretical explanations.

Scenario 1: Refactoring Existing Code

In a legacy project, I found a complex function with multiple responsibilities mixed together.

// Before
function processUserData(data) {
  if (data && data.user && data.user.id) {
    // 1. 데이터 유효성 검증
    const user = data.user;
    if (user.age < 18) {
      console.log('User is a minor.');
      return null;
    }
    // 2. 데이터베이스에서 추가 정보 조회
    const details = db.fetchDetails(user.id);
    // 3. 사용자 정보와 추가 정보 조합
    const combinedData = { ...user, ...details };
    // 4. 최종 데이터 포맷팅
    return `Name: ${combinedData.name}, Age: ${combinedData.age}, Status: ${combinedData.status}`;
  }
  return null;
}

I selected this code block, pressed Cmd+K, and typed: “Refactor this function by splitting it into smaller functions based on their roles and improve readability. Use ES6 syntax.”

// After
const MINIMUM_AGE = 18;

function validateUser(user) {
  if (!user || !user.id || user.age < MINIMUM_AGE) {
    console.log('Invalid or underage user.');
    return false;
  }
  return true;
}

function combineUserData(user, db) {
  const details = db.fetchDetails(user.id);
  return { ...user, ...details };
}

function formatUserOutput(userData) {
  const { name, age, status } = userData;
  return `Name: ${name}, Age: ${age}, Status: ${status}`;
}

function processUserData(data, db) {
  const user = data?.user;

  if (!validateUser(user)) {
    return null;
  }

  const combinedData = combineUserData(user, db);
  return formatUserOutput(combinedData);
}

The result was surprisingly good. Each responsibility was separated into a clear function, and the code quality was significantly improved by using constants and other best practices. Of course, it’s essential to review the generated code and not trust it 100%, especially to ensure there are no errors in the business logic.

Scenario 2: Adding a Feature with Codebase Reference

Let’s take the example of adding a new API endpoint. Previously, this would involve opening the relevant controller, service, and model files to understand their structure before writing any code.

With Cursor, I made the following request in the Cmd+L chat panel:

Referencing the structure of @routes/user.js and @services/userService.js, I want to add an API endpoint to change a user’s profile image. Use the path PUT /users/:id/profile-image and assume the request body contains imageUrl. Please draft the necessary router and service functions.

Cursor understood the code style and structure of the two files and generated a code snippet that matched. This significantly reduced development time by providing code that was consistent with the existing codebase.

Failure Case: Grasping the Context of a Massive Codebase

However, the @Codebase feature wasn’t a silver bullet. When I asked it to reference an entire monolithic project consisting of hundreds of files, the response time slowed down considerably, and it often generated incorrect code based on irrelevant files.

This is due to the context window limitations of LLMs. In the end, it was more effective to explicitly specify a few key files with the @ symbol rather than using a broad reference like @Codebase. This shows that prompt engineering skills—selecting and providing the right information to the AI—are still crucial.

Pros, Cons, and the Trade-off

Pros

Deep Integration: AI acts as a core part of the IDE, not just an assistant, enabling a very natural interaction.
Precise Context Management: The @ symbol allows you to clearly specify sources for the AI to reference, improving the accuracy of the output.
Flexible Model Selection: You can freely choose models from various LLM providers like OpenAI and Anthropic and manage costs with your own API key.
Familiar Usability: Being based on VS Code, you can use all your existing shortcuts, extensions, and themes.

Cons

Performance: It can feel heavier than standard VS Code, especially when indexing files or analyzing the codebase in large projects.
Stability: It can occasionally be unstable, with the UI freezing or the AI failing to respond.
Context Limitations: The @Codebase feature becomes less practical as the project size grows.

In conclusion, Cursor represents a trade-off: you sacrifice a bit of stability and performance for a massive boost in code writing and editing speed, powered by AI.

Conclusion: Who is Cursor For?

Cursor may not be the right answer for every developer. If you just want a few lines of code autocompleted, GitHub Copilot is sufficient.

However, for developers who want to actively leverage AI throughout the entire development process—whether for rapid prototyping of new features, analyzing unfamiliar codebases, or automating repetitive refactoring tasks—Cursor can be a powerful weapon. It transforms the developer’s role from someone who types code to an architect who gives clear instructions to an AI and reviews the results.

Cursor is an exciting experiment that shows what the future of coding with AI might look like. Even if it doesn’t replace your primary editor today, it’s well worth taking the time to experience it.

Jiwon Min Developer