Cursor is an AI-first code editor built on VS Code with deep codebase understanding and multi-file editing capabilities. Mastering Cursor Rules, context attachments, and Composer enables AI-powered development workflows.
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What is the purpose of a .cursorrules file (or .cursor/rules/ directory) in a project?
The .cursorrules file (or .cursor/rules/ directory in newer versions) contains persistent project-level AI instructions that Cursor injects as context for every AI chat and inline edit session. Teams use it to encode tech stack details, coding conventions, architectural patterns, and constraints so the AI consistently follows project standards.
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A developer uses @codebase in Cursor's chat. What does this context attachment do?
@codebase enables codebase-wide context retrieval — Cursor searches the entire project for relevant code and includes it in the AI's context window. This lets the AI answer questions about how features are implemented elsewhere, find similar patterns, and make suggestions consistent with the broader codebase rather than just the visible files.
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What is Cursor's Composer feature designed for?
Cursor's Composer is designed for multi-file code generation. Unlike inline AI edits (which modify the current file), Composer can create new files, modify multiple existing files, and execute a sequence of changes to implement a complete feature. It operates with broader project awareness and shows all proposed changes before applying them.
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When applying AI suggestions in Cursor, what does pressing Tab vs Cmd+K do?
Tab in Cursor accepts inline ghost text completions — the small, single-location suggestions that appear as you type (similar to GitHub Copilot). Cmd+K (or Ctrl+K) opens the inline AI edit panel where you describe a larger change in natural language and Cursor generates and shows a diff for the selected code block.
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A developer adds @Docs referencing the React documentation URL in Cursor. What does this enable?
Cursor's @Docs feature lets you index external documentation (by URL) and reference it in chat. When you use @Docs, Cursor retrieves relevant sections from the indexed docs and includes them as context. This means AI suggestions can be grounded in accurate, up-to-date library documentation rather than potentially outdated training data.