Cursor AI Features Explained: What You Actually Get

Feature lists tell you what a tool has. They do not tell you what actually matters or how to use capabilities effectively. Cursor AI includes features that sound similar on paper but serve completely different purposes in practice.

This guide cuts through the marketing language to explain what each Cursor feature actually does, when each one helps most, and which capabilities deliver the most value. You will understand not just the feature names but how they fit into real development workflows. For a comprehensive understanding of Cursor AI, you can explore gudie on how to use Cursor AI.

Whether you are evaluating Cursor before signing up or already using it and want to get more value, knowing the features deeply makes the difference between occasional use and genuine productivity gains.

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What Are Cursor's Core AI Features?

Cursor organizes its AI capabilities into distinct features that serve different purposes during development.

What is Cursor Tab autocomplete?

Quick Answer: Cursor Tab is the AI autocomplete feature that predicts and suggests code as you type, accepting suggestions by pressing Tab to insert single lines or entire blocks of contextually relevant code.

Tab autocomplete represents the most frequent AI interaction for most developers. As you type, Cursor analyzes your current context and predicts what comes next. Suggestions appear as grayed text that you accept with the Tab key or dismiss by continuing to type.

The predictions range from completing variable names to suggesting entire function implementations. Tab uses your current file, open tabs, and project context to make relevant suggestions rather than generic completions.

Cursor's Tab feature competes directly with GitHub Copilot's autocomplete. Both use similar underlying models, but Cursor's implementation includes awareness of your broader codebase through its indexing system.

If you're curious about how Cursor AI integrates with VS Code, check out guide on: is Cursor AI a VS Code fork.

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What is Cursor Composer?

Quick Answer: Composer is Cursor's multi-file AI editing feature that lets you describe changes in natural language and generates coordinated edits across multiple files with diffs you review before applying.

Composer handles the big tasks. When you need to add a new feature, refactor a component, or make changes that span multiple files, Composer takes natural language instructions and produces the code changes.

You access Composer through a keyboard shortcut or command palette. A panel opens where you describe what you want. Cursor analyzes your request against your codebase and generates changes, showing diffs for each affected file.

The review step matters. Composer shows exactly what will change before applying anything. You can accept all changes, reject them, or modify specific parts. This keeps you in control while automating the tedious work of finding and updating code across files.

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What is Cursor Chat?

Quick Answer: Chat is an integrated AI conversation interface where you can ask questions about your code, get explanations, debug errors, and receive suggestions while maintaining context about your current project.

Chat provides a conversational interface for interacting with AI about your code. Unlike Composer which generates edits, Chat focuses on discussion and explanation.

Typical Chat uses include:

• Asking what a piece of code does
• Debugging error messages by pasting them and asking for analysis
• Getting suggestions for how to approach a problem
• Understanding unfamiliar libraries or patterns in your codebase

Chat maintains conversation history within a session, so you can have back-and-forth discussions that build on previous messages. The AI remembers what you discussed earlier in the conversation.

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How does codebase indexing work in Cursor?

Quick Answer: Cursor automatically indexes your project files to build understanding of code structure, relationships, and patterns, enabling AI features to reference your entire codebase rather than just the current file.

Indexing happens in the background when you open a project. Cursor analyzes file contents, function definitions, import statements, and relationships between components. This creates a searchable knowledge base about your specific codebase.

The index enables context-aware responses. When you ask Chat about a function, it can find where that function is defined, where it is used, and what depends on it. Composer uses the index to understand which files need changes when you request modifications.

Indexing runs locally on your machine. The index updates as you modify files, staying current with your actual code. Large codebases take longer to index initially but updates are incremental.

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How Do Cursor's AI Features Work Together?

The features complement each other for different stages and scales of work.

When should you use Tab versus Chat versus Composer?

Quick Answer: Use Tab for line-by-line coding flow, Chat for questions and exploration, and Composer for multi-file changes or generating new code from descriptions.

Tab works best for:

• Writing code when you know what you want
• Accepting boilerplate completions
• Maintaining coding flow without interruption
• Small, predictable code patterns

Chat works best for:

• Understanding unfamiliar code
• Debugging specific errors
• Exploring approaches before committing
• Learning how something works

Composer works best for:

• Creating new features spanning multiple files
• Refactoring code across components
• Implementing changes described in requirements
• Making coordinated edits that affect multiple places

The distinctions matter for efficiency. Using Composer for a one-line change wastes time. Using Tab for complex refactoring produces fragmented results.

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Can you reference specific files in AI conversations?

Quick Answer: Yes, you can use @ mentions to reference specific files, functions, folders, or documentation in Chat and Composer, giving the AI precise context for your questions or requests.

The @ symbol triggers context referencing. Type @ followed by a filename, and Cursor shows matching files from your project. Select one to include it in your prompt.

This targeting improves response quality dramatically. Instead of the AI guessing which files matter, you explicitly tell it. A question like "why is @utils/auth.ts throwing this error" gives the AI exactly the context it needs.

You can reference multiple items in a single prompt. For Composer requests affecting specific files, referencing them explicitly produces more accurate changes.

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Does Cursor remember previous conversations?

Quick Answer: Cursor maintains context within a Chat session but does not persist conversations across editor restarts by default, though you can enable conversation history in settings.

Within a session, Chat remembers everything discussed. You can ask follow-up questions, reference previous suggestions, and build on earlier explanations. The AI maintains this context throughout your working session.

By default, closing Cursor or starting a new chat clears this history. For ongoing work where context matters across sessions, settings allow enabling persistent history.

Composer sessions are inherently temporary. Each Composer interaction is a discrete request with a specific output. Previous Composer generations do not automatically inform new ones.

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What Advanced Features Does Cursor Offer?

Beyond core capabilities, several features address specific development needs.

What AI models can you use in Cursor?

Quick Answer: Cursor supports multiple AI models including GPT-4, GPT-4o, Claude 3.5 Sonnet, Claude 3 Opus, and allows switching between them based on task requirements or preference.

Model selection affects response quality, speed, and cost. Different models excel at different tasks:

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Does Cursor have a terminal integration?

Quick Answer: Yes, Cursor includes integrated terminal with AI assistance that can suggest commands, explain terminal output, and help debug command-line errors within your development workflow.

The terminal in Cursor works like VS Code's integrated terminal with AI enhancements. You can ask the AI to suggest commands for tasks you describe, explain what complex commands do, or help debug error output from terminal operations.

This integration matters for workflows involving build tools, git operations, package managers, and deployment scripts. Instead of switching to documentation or a search engine, you can ask Cursor directly.

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What is Cursor's Privacy Mode?

Quick Answer: Privacy Mode prevents your code from being sent to external AI services, keeping all code local while limiting AI functionality to features that can run without external model access.

Privacy Mode addresses security concerns for sensitive codebases. When enabled, Cursor does not send code context to OpenAI, Anthropic, or other model providers. Your code stays on your machine.

The tradeoff is reduced AI capability. Features requiring model inference do not work without sending code to those models. Privacy Mode essentially gives you VS Code functionality plus local features only.

Teams working with proprietary code, regulated industries, or security-sensitive projects use Privacy Mode when AI benefits do not outweigh data exposure concerns.

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Can you customize Cursor's AI behavior?

Quick Answer: Yes, Cursor allows customizing AI behavior through rules files, system prompts, and settings that influence how the AI responds to your specific coding style and project requirements.

Customization options include:

• Rules files: Project-specific instructions that tell the AI about your coding conventions, preferred patterns, and project context.
• System prompt settings: Global instructions that apply across all projects for your personal preferences.
• Model selection defaults: Choose which model handles which features by default.
• Autocomplete aggressiveness: Control how often Tab suggestions appear and how much code they suggest.

These customizations help Cursor match your workflow rather than forcing you to adapt to default behavior.

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How Does Cursor Handle Different Programming Languages?

Language support varies in depth and quality across different technologies.

Which programming languages work best with Cursor?

Quick Answer: Python, JavaScript, TypeScript, and other popular languages with extensive training data produce the best AI suggestions, while less common languages work but with reduced accuracy.

AI model training data skews toward popular languages with large open-source codebases. This means:

• Excellent support: Python, JavaScript, TypeScript, Java, C#, Go, Rust
• Good support: PHP, Ruby, Swift, Kotlin, C++
• Basic support: Niche languages, domain-specific languages, older languages

Excellent support means accurate completions, good understanding of idioms, and relevant suggestions. Basic support means the AI can help but produces more generic or occasionally incorrect suggestions.

At LowCode Agency, we work across multiple technology stacks and find AI tools most helpful for mainstream languages where model training includes extensive examples.

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Does Cursor understand framework-specific patterns?

Quick Answer: Cursor recognizes common framework patterns for popular technologies like React, Vue, Django, Express, and Next.js, suggesting code that follows framework conventions.

Framework awareness comes from training data including framework-specific codebases. When you write React components, Cursor understands JSX syntax, hook patterns, and component lifecycle. Django projects get appropriate suggestions for models, views, and templates.

The @ reference feature helps here. If you reference documentation or example files from a framework, the AI incorporates that context into its suggestions.

Newer frameworks or custom internal frameworks have less built-in understanding. You can improve this through rules files that explain your specific patterns.

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Why Founders Choose LowCode Agency for AI-Assisted Development

Cursor AI has powerful features. It can autocomplete code, refactor functions, and generate logic faster than traditional workflows. But features alone do not build scalable products. AI helps you write code. It does not design architecture, define data models, or plan long-term infrastructure.

• AI accelerates development, not system design
  Cursor can generate components quickly, but scalable SaaS apps still require structured database planning, role-based access control, and performance-aware architecture.
• We design before we build
  We define workflows, user permissions, API structure, and backend logic first so AI-generated code fits into a stable system.
• Prototype fast, then scale properly
  We help founders use AI tools to validate ideas quickly, then evolve the product using low-code platforms or full-code stacks when scale demands it.
• 350+ custom apps built across industries
  At LowCode Agency, we’ve delivered 350+ business apps, SaaS platforms, dashboards, automation systems, and AI-powered tools that teams rely on daily.

Cursor AI is powerful. But building something reliable requires product thinking beyond code generation. If you want to move fast without creating technical debt, let’s discuss your roadmap and structure it the right way.

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AI App Development

Your Business. Powered by AI

We build AI-driven apps that don’t just solve problems—they transform how people experience your product.

Let's talk

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Conclusion

Cursor packs substantial AI capability into features that serve distinct purposes. Tab keeps you in flow during active coding. Chat answers questions and explains code. Composer handles the heavy lifting of multi-file changes. Codebase indexing ties everything together with project-wide context.

Understanding which feature fits which task matters more than knowing the feature list. Start with Tab for everyday coding. Graduate to Chat when you have questions.

Use Composer for changes too big to handle manually. The features compound when used appropriately rather than competing for your attention.