How Cursor (AI IDE) Works – A Deep Dive into AI Coding Tools

AI-powered IDEs like Cursor, Windsurf, and Copilot are transforming the way developers write code, generating up to 70% of some users' projects. But to get the most out of these tools, it's crucial to understand how they operate under the hood. Cursor, for example, isn't just a fancy auto-complete. It's an agent-based system built around an LLM (like Anthropic’s Claude 3.5 Sonnet), optimized through a combination of prompt engineering and tool use. At its core, Cursor wraps around a forked version of VSCode, enhancing it with chat UI, file readers, and command execution tools. The key to using Cursor effectively lies in structuring codebases, comments, and request prompts in a way that makes it easier for the AI to deliver accurate and useful code. Author Shrivu pulls back the curtain on how these AI coding partners work—and how developers can make them work better.

Key Takeaways:

• LLMs Predict Code, Not "Think" – AI IDEs like Cursor function by predicting the next logical token in code, similar to auto-complete but with layers of tool interaction. Instead of just generating text, these systems rely on tool calling to read, write, and search files dynamically.

• Building an AI IDE – Cursor is essentially a fork of VSCode with added capabilities:

  • LLM-powered chat UI
  • Codebase search tools (grep_search, file_search)
  • File reading/writing and command execution (read_file(), write_file(), run_command())

• Optimization for Accuracy – One major challenge AI IDEs face is producing perfect code without errors. Cursor combats this with:

• Semantic Diffs – Instead of rewriting whole files, it suggests small, context-aware changes with inline code comments.

• Specialized LLMs for Editing – A dedicated smaller LLM applies code changes while fixing syntax errors.

• Linter Feedback Loops – AI auto-corrects its edits based on linter feedback.

• Using Cursor Effectively:

  • Be explicit with context – Use @file or @folder to guide the AI’s attention and reduce ambiguity.
  • Leverage code comments and file organization – Good inline documentation makes AI-powered indexing and searching far more effective.
  • Avoid large files for edits – Files over 500 lines can slow AI and introduce more errors.
  • Use well-tested linting tools – The quality of linter feedback directly affects Cursor’s accuracy.
  • Choose the right models – Some LLMs are better at structured, multi-step coding tasks. Anthropic’s models perform particularly well in Cursor.

• Best Practices for "Cursor Rules" –

  • Think of rules as encyclopedia articles, not one-off commands.
  • Avoid redundancies—rules should guide, not micromanage.
  • LLMs work best with positive reinforcement (what to do) rather than negative restrictions (what not to do).
  • Cursor rules should be designed for the AI to fetch, using concise and meaningful names and descriptions.

• The Future of AI IDEs – Despite Cursor’s success (valued close to $10 billion), the author speculates whether AI-first companies like Anthropic might develop their own integrated IDEs instead of relying on third-party tools. Regardless, developers who structure their codebases and workflows for AI will thrive in the increasingly code-automated world.

“If Cursor isn’t working for you, you are using it wrong.”

By understanding how AI IDEs function, developers gain a legitimate "cheat code" for making these tools far more reliable, especially in massive codebases.
Link to Article