Scalable Human Blog

Cursor hallucinates when it lacks structured, scoped context—leading to broken logic and messy code. The fix? Ditch the generic .cursorrules file and use modular, purpose-built .mdc rule files that guide the AI like a junior dev on your team.

🎯 Why Cursor Hallucinates and How to Fix It
If you’ve used Cursor to build software, you’ve probably seen it go off the rails—creating random folders, misinterpreting your stack, or generating UI components that don’t match your design system. This isn’t Cursor being “bad”; it’s Cursor being uninformed. Like any junior developer, it needs clear, scoped instructions. Without them, it fills in the blanks—aka hallucination.

In this post, I’ll explain why Cursor hallucinates and share a rules-based system.

The Problem: Context Chaos = Hallucination

AI models like those powering Cursor (Claude, Gemini, GPT) rely on context to generate accurate code. When that context is vague or missing, the model improvises—leading to:

• Inconsistent folder structures
• Broken logic flows
• Hallucinated APIs or UI components
• Misaligned tech implementation

This happens because the AI is trying to guess what you want based on incomplete or overly broad instructions.

The Misstep: Why .cursorrules Falls Short

Cursor provides a .cursorrules file to define project-wide rules. Sounds helpful, but in practice, it often causes more harm than good:

• One-size-fits-all logic: Global rules are too generic to guide specific tasks.
• Cognitive overload: The AI can’t reliably process a long, unstructured file.
• Hallucination trigger: Broad rules lead to vague outputs, especially in large projects.

The Fix: Scoped Rules with .mdc Files

Instead of relying on vague prompts or a bloated .cursorrules, I use a modular system of .mdc files stored in .cursor/rules/. These files act like scoped documentation the AI can reliably reference.

Here are the seven files to generate before writing a single line of code:

1. backend_structure_document.mdc – Defines API routes, DB schema, and logic patterns.
2. app_flow_document.mdc – Describes the user journey and how each screen connects.
3. tech_stack_document.mdc – Outlines libraries, frameworks, and architectural decisions.
4. frontend_guidelines_document.mdc – Sets component styling, naming, and layout rules.
5. backend_structure.mdc – Provides backend implementation details and logic flows.
6. implementation_plan.mdc – A step-by-step build plan for the AI to follow.
7. cursor_project_rules.mdc – Defines scoped global standards (naming, error handling, etc.).

Each file gives Cursor a specific lens to view your project—like giving a junior dev a tailored onboarding doc for each area.

The Model Match: Using the Right AI for the Job

Not all models are created equal. I now use:

• Gemini Pro 2.5 – For scanning the full codebase (1M token context), updating .mdc files, and catching architectural issues.
• Claude Sonnet 3.5/3.7 – For executing features, fixing logic bugs, and building from the implementation plan.

Think of it this way: Gemini thinks. Sonnet builds.

✅ Key Takeaways

• Hallucination stems from vague or missing context—not model failure.
• Avoid .cursorrules for large projects—it’s too broad and hard for the AI to parse.
• Use scoped .mdc files to provide modular, digestible rules the AI can follow.
• Start with structure—generate your .mdc docs before any code.
• Pair models smartly—use Gemini for analysis, Claude for execution.

🎉 Conclusion
Cursor is powerful, but only if you treat it like a junior developer: give it the right tools, structure, and guidance. By replacing generic prompts and bloated rule files with scoped .mdc documents, you train the AI to understand your product—not guess at it. The result? Cleaner code, fewer hallucinations, and a dev assistant that actually feels like part of your team.

Have your own system for taming Cursor? Share it in the comments—I’d love to learn how others are solving this too.