Stop Cursor AI from Writing Python in Your JavaScript (2025 Guide)

You’re deep in a React refactor, you ask Cursor to "generate a loop that filters this array," and instead of a clean .filter() method, it gives you a for x in list: block.

Your file extension is .tsx. Your whole project is JavaScript. Yet, for some reason, the AI has decided that today is a Python day.

This is the "Language Drift" problem. It is one of the most jarring and confusing hallucinations in AI-assisted development. One moment you're moving at light speed, and the next, you're manually translating list comprehensions back into arrow functions.

For "Vibe Coders," this is a classic momentum-breaker. You trust the tool to understand your environment, but when it starts mixing syntax from different ecosystems, it’s a sign that the AI has lost its "grounding."

In this guide, we’ll explain why Cursor AI occasionally writes Python in your JavaScript files, identify the root causes of these syntax hallucinations, and provide the "Grounding Protocols" to keep your AI focused on the correct language.

Why LLMs Hallucinate the Wrong Syntax

To stop the Python drift, you have to understand the mind of the model.

Large Language Models (LLMs) like Claude 3.5 Sonnet and GPT-4o are "polyglots"—they speak dozens of languages fluently. When they generate code, they aren't "running" a compiler; they are predicting the next most likely piece of text based on the context they've been given.

If you are working in a large repository that contains a mix of Python (maybe for data scripts) and JavaScript (for the frontend), the AI's "Context Window" can become a blurred mess of both. If the last 10 files it indexed were Python, its internal "vibe" is tilted toward Python syntax, even if the current file is .js.

Root Cause 1: Lack of File Context in 'Composer' Mode

Cursor’s Composer mode (Cmd+I) is incredibly powerful because it can edit multiple files at once. However, because it is "zoomed out" to look at your whole project, it occasionally forgets the specific rules of the individual file it is currently editing.

If you give a vague instruction like "make the data processing more efficient," the AI might default to its "favorite" way of processing data—which, for many models trained on data science libraries, is Python.

Root Cause 2: Polyglot Repositories

If your folder structure looks like this:

/my-project
  /api (Python/FastAPI)
  /web (Next.js/TS)

The AI is constantly switching mental gears. When you are in the /web folder, the AI is still "seeing" the patterns from the /api folder in its background memory. This "leakage" is what causes the AI to suddenly write a Python decorator inside a React component.

The 10-Second Fix: Explicit File Header Comments

The fastest way to re-ground a drifting AI is to use a Language Header. This is a simple comment at the very top of your file that acts as a permanent anchor for the model's attention.

Add this to the first line of your JS/TS files:

// Language: TypeScript
// Stack: Next.js App Router, Tailwind CSS

When the AI reads the file to prepare a fix, it sees this instruction immediately. It’s like a signpost that tells the model: "You are now in TypeScript territory. Put the Python away."

Using .cursorrules to Enforce Language Boundaries

If header comments are too tedious, you can use the .cursorrules file to set a global language policy for your project. This is the most robust way to prevent hallucinations.

Add this to your .cursorrules:

# Language Enforcement
- In the /web directory, NEVER use Python syntax. Always use TypeScript.
- Prefer modern ES6+ features (optional chaining, destructuring).
- If you are unsure of the syntax, check the nearest .tsconfig.json.

By explicitly defining directory-level rules, you prevent the "leakage" between your backend and frontend code. For a deeper look at how to set these up, check our guide on .cursorrules.

Advanced: Using @Files to Re-ground the AI

If the AI starts hallucinating Python in the middle of a chat session, it means its internal "conversation history" has become corrupted with wrong patterns.

The Fix: Use the @ symbol to force the AI to look at specific files. Instead of: "Fix the loop in this file." Try: "Fix the loop in @currentFile using the patterns found in @main-utils.ts."

By providing a "Golden Example" of the correct language, you force the AI to match the syntax of the reference file instead of its own internal predictions.

FlowQL: Senior Debugging for Multi-Language Systems

Building "Polyglot" systems—apps that mix different languages and frameworks—is the "last 20%" of software engineering. AI assistants are great at the individual components, but they frequently fail at the boundaries where different syntaxes meet.

At FlowQL, we help developers manage these complex, multi-language codebases. Whether you’re trying to connect a Python AI backend to a TypeScript frontend and your AI is hallucinating code that doesn't exist, we provide the human expertise to clean up the syntax drift and secure your integrations.

If your AI tool has turned your project into a "Spaghetti Language" mess, it's time for a professional audit.

Conclusion

Cursor AI writing the wrong language is a sign of Context Fatigue. By using file headers, .cursorrules, and explicit @ references, you can keep your AI focused, accurate, and syntax-correct.

Don't let a language hallucination slow you down. [Book a session with FlowQL] and let’s get your polyglot project running perfectly.

---

FAQ: AI Language Hallucinations

Q: Why does my AI assistant prefer Python? A: Many LLMs have a slight bias toward Python because it is the most common language in the open-source datasets and documentation they were trained on.

Q: Can I turn off Python support in Cursor? A: No, but you can effectively "mute" it for specific projects by using the language enforcement rules in your .cursorrules file.

Q: Does this happen in other AI editors? A: Yes. GitHub Copilot, ChatGPT, and Claude all suffer from language drift in complex or polyglot environments. It is a fundamental characteristic of LLM-based coding.