TL;DR - 3-4 sentence summary covering what Cursor is, who it’s for, key differentiators from VS Code/Copilot, and what readers will learn

Cursor is an AI-first code editor built on VS Code’s foundation that transforms how you write, refactor, and debug code through deep AI integration. Unlike GitHub Copilot’s autocomplete-focused approach, Cursor provides multi-file editing, codebase-aware chat, and natural language commands that can modify entire functions or generate complex implementations across your project. It’s designed for professional developers who want AI assistance that understands context beyond the current file—whether you’re building a Kubernetes operator, refactoring a React component tree, or writing Terraform modules.

The key differentiator is Cursor’s Composer feature, which can edit multiple files simultaneously based on natural language instructions, and its codebase indexing that allows the AI to reference your entire project structure when making suggestions. While GitHub Copilot excels at single-line completions, Cursor can scaffold entire API endpoints, update test files to match implementation changes, or refactor authentication logic across controllers, middleware, and configuration files in one operation.

This guide walks you through installation, configuration, and practical workflows including setting up custom AI rules for your tech stack (Python/FastAPI, Go microservices, TypeScript/Next.js), integrating with existing VS Code extensions, and establishing safe practices for AI-generated infrastructure code. You’ll learn how to use Cursor’s chat modes effectively, configure model preferences (GPT-4, Claude 3.5 Sonnet, or local models), and build prompt templates for common tasks like writing Ansible playbooks or generating Prometheus alerting rules.

Critical safety note: Always review AI-generated system commands, database migrations, and infrastructure-as-code before execution. Cursor can hallucinate package names, generate incorrect kubectl commands, or suggest destructive operations. Validate all suggestions in development environments first, especially for Terraform applies, Docker deployments, or any commands affecting production systems.

What is Cursor IDE and Why It Matters in 2026 - Overview of Cursor’s evolution, its fork from VS Code, and how it differs from traditional AI coding assistants with its composer and chat-first approach

Cursor IDE emerged in 2023 as a fork of Visual Studio Code, but by 2026 it has evolved into the dominant AI-native development environment. Unlike traditional AI coding assistants that bolt onto existing editors, Cursor rebuilt VS Code’s core with AI-first architecture.

Cursor’s Composer feature distinguishes it from GitHub Copilot and other autocomplete-focused tools. Instead of suggesting line-by-line completions, Composer generates entire features across multiple files. You can request “Add Prometheus metrics to this FastAPI service” and watch it modify main.py, create metrics.py, and update requirements.txt simultaneously.

# Composer can generate complete monitoring setup
from prometheus_client import Counter, Histogram
import time

request_count = Counter('api_requests_total', 'Total API requests')
request_duration = Histogram('api_request_duration_seconds', 'Request duration')

Chat-First Workflow

Unlike Copilot’s inline suggestions, Cursor centers on conversational development. The chat panel understands your entire codebase context—it indexes your Terraform modules, Ansible playbooks, and Docker configurations. Ask “Why is my Kubernetes deployment failing?” and it analyzes your YAML manifests with full repository awareness.

# Cursor can explain and fix infrastructure commands
kubectl get pods -n production --field-selector status.phase=Failed

⚠️ Caution: Always validate AI-generated infrastructure commands before running on production systems. Cursor may hallucinate kubectl contexts or Terraform workspace names.

Beyond Autocomplete

By 2026, Cursor integrates with Claude 3.5 Sonnet, GPT-4, and custom models through its API. You can switch models mid-conversation, use prompt templates for code reviews, and maintain conversation history across sessions. This makes it fundamentally different from traditional assistants—it’s a collaborative development partner, not just a smart autocomplete.

Cursor vs GitHub Copilot vs Windsurf: Feature Comparison - Side-by-side comparison of autocomplete quality, chat interfaces, codebase understanding, and pricing models to help developers choose the right tool

Choosing between AI coding assistants requires understanding their core strengths. Here’s how the leading tools compare in 2026:

Cursor and GitHub Copilot both use GPT-4-class models for inline suggestions, with Cursor offering slightly faster response times (typically 150-300ms vs 300-500ms). Windsurf’s Cascade model excels at multi-file edits, automatically detecting when changes in api/routes.py require updates to tests/test_routes.py.

# All three tools handle context-aware completions well
def deploy_terraform(environment: str):
    # Cursor/Copilot/Windsurf all suggest:
    subprocess.run(["terraform", "apply", 
                   f"-var-file={environment}.tfvars"])

Codebase Understanding

Cursor’s @Codebase feature indexes your entire project, enabling queries like “Where do we handle Prometheus metrics?” Windsurf’s Flows feature goes further by tracking multi-step refactoring across dozens of files. GitHub Copilot Chat requires manual context selection but integrates seamlessly with GitHub Issues and Pull Requests.

Chat Interfaces

Cursor provides inline chat (Cmd+K) and sidebar chat with Claude 3.5 Sonnet or GPT-4. Windsurf’s Cascade mode handles complex requests like “Refactor our Ansible playbooks to use roles” autonomously. GitHub Copilot Chat works directly in VS Code but lacks Cursor’s composer mode for multi-file generation.

Pricing Models

  • GitHub Copilot: $10/month individual, $19/month business
  • Cursor: $20/month Pro (500 fast requests), $40/month Business
  • Windsurf: $15/month Pro, $30/month Teams

⚠️ Critical Warning: Always review AI-generated infrastructure commands before execution. Validate Terraform plans, test Ansible playbooks in staging, and never run kubectl delete or rm -rf commands without verification. AI tools can hallucinate destructive operations.

Understanding Cursor’s Core Features - Deep dive into Cmd+K inline editing, Composer for multi-file changes, @ symbols for context, and the AI models available (GPT-4, Claude 3.5 Sonnet, etc.)

Cursor’s power lies in four interconnected features that transform how you write code. Cmd+K (Ctrl+K on Windows/Linux) enables inline editing within your current file. Highlight a function, press Cmd+K, and request changes like “add error handling for network timeouts” or “convert this to use async/await.” The AI modifies code in place while preserving your surrounding context.

Composer (Cmd+I) handles multi-file operations. Open Composer to request changes spanning multiple files: “refactor authentication logic from Express middleware into a separate auth service with TypeScript types.” Composer shows a diff view across all affected files before applying changes, crucial when modifying Terraform modules or Ansible playbooks that reference each other.

The @ symbol system gives Cursor precise context:

  • @Files - Reference specific files: “Update @database.py to use connection pooling”
  • @Folders - Include entire directories: “Refactor @src/api following REST conventions”
  • @Code - Reference functions or classes: “Add logging to @handlePayment”
  • @Docs - Pull from documentation: “Implement Prometheus metrics following @Docs”
  • @Web - Search current information: “Use @Web to find latest FastAPI authentication patterns”
# Example: Using @Code context
# Prompt: "Add retry logic to @fetch_metrics with exponential backoff"

async def fetch_metrics(endpoint: str, max_retries: int = 3):
    for attempt in range(max_retries):
        try:
            return await client.get(endpoint)
        except httpx.TimeoutError:
            await asyncio.sleep(2 ** attempt)

Model Selection

Cursor supports GPT-4o, Claude 3.5 Sonnet, and o1-preview. Claude 3.5 Sonnet excels at complex refactoring and understanding large codebases. GPT-4o offers faster responses for routine tasks. Access model selection in Settings → Models.

⚠️ Critical: Always review AI-generated infrastructure commands before execution. Validate Kubernetes manifests, Terraform plans, and database migrations in staging environments first.

Installation and Initial Setup - Step-by-step guide covering download, license activation, importing VS Code settings/extensions, configuring AI models, and setting up .cursorrules files

Visit cursor.sh and download the installer for your operating system. Cursor supports macOS, Windows, and Linux. The installation process mirrors VS Code—simply run the installer and follow the prompts.

On first launch, Cursor prompts you to import your VS Code settings and extensions. Click “Import from VS Code” to transfer your existing configuration, including themes, keybindings, and installed extensions. This migration typically completes in under a minute.

License Activation and Model Configuration

Navigate to Settings (Cmd/Ctrl + ,) and select the “Cursor” tab. Enter your license key or start a free trial. Under “AI Models,” configure your preferred providers:

# Example model configuration
Primary Model: GPT-4 Turbo
Fast Model: Claude 3.5 Sonnet
Embedding Model: text-embedding-3-small

For enterprise users, add your OpenAI or Anthropic API keys under “Advanced Settings” to use your own quota instead of Cursor’s shared pool.

Setting Up .cursorrules Files

Create a .cursorrules file in your project root to provide context-specific instructions to the AI:

# .cursorrules
Project: Kubernetes infrastructure automation
Stack: Terraform, Ansible, Prometheus
Guidelines:
- Always validate Terraform plans before applying
- Use ansible-lint for playbook validation
- Include monitoring alerts for all new services
- Never expose secrets in code

⚠️ Critical Warning: Always review AI-generated infrastructure commands before execution. The AI may hallucinate Terraform resource names, Ansible module parameters, or kubectl commands that could disrupt production systems. Run terraform plan and ansible-playbook --check to validate changes in staging environments first.

For Python projects, add language-specific rules:

# .cursorrules for Python
- Use type hints (PEP 484)
- Follow Black formatting
- Include pytest fixtures for database tests

Essential Configuration for Professional Workflows - Best practices for privacy settings, configuring codebase indexing, setting up custom rules, managing API keys, and team settings

Start by reviewing Settings → Privacy to control what data Cursor sends to its servers. Disable telemetry and crash reporting if working with proprietary code. For sensitive projects, enable Privacy Mode which prevents code snippets from being sent to AI models for training.

Configure .cursorignore in your project root to exclude sensitive files from indexing:

# .cursorignore
.env
secrets/
*.key
terraform.tfstate
ansible-vault.yml

Codebase Indexing Configuration

Cursor indexes your codebase for context-aware suggestions. For large monorepos, limit indexing scope in Settings → Features → Codebase Indexing. Exclude build artifacts and dependencies:

# .cursor/settings.json
{
  "indexing.exclude": [
    "**/node_modules/**",
    "**/dist/**",
    "**/.terraform/**",
    "**/target/**"
  ]
}

Custom Rules and Prompt Templates

Create project-specific AI behavior with .cursorrules files:

# .cursorrules
- Always use TypeScript strict mode
- Prefer Zod for runtime validation
- Include error handling for all Prometheus metric exports
- Add JSDoc comments for public APIs

API Key Management

Store API keys in Settings → Models rather than environment variables. For team environments, use separate keys for development and production. Rotate keys quarterly and never commit them to version control.

⚠️ Critical Warning: Always validate AI-generated infrastructure commands before execution. Review Terraform plans, Ansible playbooks, and Kubernetes manifests manually. AI models can hallucinate destructive operations like terraform destroy or incorrect kubectl delete commands that could impact production systems.

Team Settings

For team consistency, commit .cursor/settings.json with shared configurations but exclude personal API keys. Use workspace settings to enforce code style rules across your team’s Cursor installations.

Real-World Workflow Examples - Four practical scenarios: refactoring legacy code, building a new feature from scratch, debugging with AI assistance, and documentation generation

Start with Cursor’s Cmd+K inline edit. Select a monolithic function and prompt: “Split this into smaller functions following single responsibility principle.” Cursor analyzes dependencies and suggests refactored code with proper error handling. Always review suggested changes—AI may miss business logic nuances in legacy systems.

# Before: 200-line function
# After AI refactoring with Cmd+K
def process_user_data(user_id):
    user = fetch_user(user_id)
    validate_permissions(user)
    return transform_output(user)

Building Features from Scratch

Use Cursor Composer (Cmd+Shift+I) for multi-file features. Prompt: “Create a REST API endpoint for user authentication with JWT tokens, including tests.” Cursor generates the FastAPI route, Pydantic models, and pytest fixtures across multiple files simultaneously.

# Cursor creates these files automatically
api/routes/auth.py
models/user.py
tests/test_auth.py

Caution: Always validate generated Dockerfile commands and deployment scripts before running in production environments.

Debugging with AI Assistance

Highlight error stack traces and use Cmd+L chat: “Explain this TypeError and suggest fixes.” Cursor references your codebase context to identify root causes. For Prometheus alerting issues, paste the alert configuration and ask: “Why isn’t this firing?” AI analyzes thresholds and query syntax.

# AI helps debug PromQL queries
- alert: HighMemoryUsage
  expr: node_memory_usage > 0.9  # AI catches missing time window

Documentation Generation

Select functions and prompt: “Generate docstrings with type hints and examples.” For infrastructure code, ask: “Document this Terraform module with usage examples and variable descriptions.” Cursor maintains consistency across your documentation style.

Warning: AI-generated Ansible playbooks or Terraform configurations should be tested in staging environments first. Verify all resource names, regions, and permissions before applying to production infrastructure.