Setting Up an AI-Native Development Workflow in 2025

The modern developer’s toolkit has changed fundamentally. AI isn’t just an add-on—it’s the foundation of how we work. Here’s how to set up an AI-native development workflow from scratch.

The Stack Overview

Our target stack combines these elements:

• Terminal: Claude Code for command-line AI
• Editor: Cursor with Claude integration
• Search: Perplexity for research
• Deployment: Vercel for instant previews
• Context: Obsidian for knowledge management

Step 1: Terminal Setup

Install Claude Code, Anthropic’s CLI tool:

# Install Claude Code
npm install -g @anthropic-ai/claude-code

# Authenticate
claude login

Configure your shell for AI-native commands:

# Add to ~/.zshrc
export ANTHROPIC_API_KEY="your-key-here"
alias cc="claude"
alias ccp="claude plan"

Step 2: Editor Configuration

Download Cursor from cursor.com, then configure it:

1. API Keys: Add your Anthropic and OpenAI keys in Settings
2. Default Model: Set Claude as your default for chat
3. Codebase Indexing: Enable for your main projects

Key settings to enable:

{
  "cursor.composer.enabled": true,
  "cursor.aiPreview.enabled": true,
  "cursor.chat.defaultModel": "claude-3.5-sonnet"
}

Step 3: Context Management

The key to effective AI assistance is providing good context. Create a .cursorrules file in your project root:

# Project Context

## Tech Stack
- Framework: Next.js 15 with App Router
- Styling: Tailwind CSS
- Database: PostgreSQL with Drizzle ORM
- Auth: NextAuth.js

## Conventions
- Use functional components with TypeScript
- Prefer server components by default
- Use Zod for validation
- Error boundaries for client components

## Project Structure
- /app - Next.js app router pages
- /components - Reusable UI components
- /lib - Utility functions and configurations
- /db - Database schema and queries

Step 4: Workflow Patterns

The Scout-Plan-Execute Loop

Before diving into code:

# 1. Scout: Understand the problem
claude "What files would I need to modify to add user authentication?"

# 2. Plan: Get a structured approach
claude plan "Implement GitHub OAuth login"

# 3. Execute: Let AI help implement
# Open Cursor and use Composer for multi-file changes

Daily Workflow

My typical day:

1. Morning Review (5 min): Ask Claude to summarize yesterday’s git commits
2. Planning (10 min): Use Claude Code to plan the day’s work
3. Implementation (3-4 hours): Cursor with AI pair programming
4. Review (30 min): AI-assisted code review before commits

Step 5: Knowledge Capture

Create an Obsidian vault for your learnings:

# AI Development Notes

## Prompting Patterns That Work
- Always provide file context
- Be specific about output format
- Include examples when possible

## Tool Quirks
- Claude excels at planning
- GPT-4 is better for quick edits
- Use Perplexity for up-to-date docs

Productivity Metrics

After implementing this workflow, I saw:

Common Pitfalls

1. Over-reliance on AI: Always review generated code
2. Poor context: AI is only as good as the information you provide
3. Ignoring security: AI can generate insecure code—use linters
4. Not learning: Don’t just accept AI output—understand it

Next Steps

Once you’re comfortable with this setup:

• Explore MCP servers for extended AI capabilities
• Set up AI-assisted testing workflows
• Create project-specific AI configurations

The goal isn’t to replace your skills—it’s to amplify them. An AI-native workflow lets you focus on architecture and creativity while AI handles the boilerplate.