How to use AI to speed up development and deliver stronger features in record time

From coding to shipping: How AI is reshaping development workflows

Development used to be about writing code line by line.
Now, AI tools are transforming how we build software—turning complex implementations into conversations, automating repetitive tasks, and helping us deliver better features faster.

However, not every AI tool delivers the same impact — and knowing how to combine their strengths is what separates fast, consistent developers from the rest.

🎯 What you'll learn

This guide shows how to combine Cursor, GitHub Copilot, and ChatGPT to move faster without sacrificing code quality.

You'll learn to:

✅ Create precise AI instructions and project context
✅ Generate automation scripts
✅ Use Plans and Agents for multi-file changes
✅ Combine models for reasoning and creativity
✅ Apply practical ChatGPT prompts for your daily dev tasks
✅ Leverage Copilot for inline code, refactoring, and documentation

🧩 Cursor: Context-aware coding at scale

Why Cursor stands out

Cursor isn't just another code editor with AI—it's built from the ground up to understand your entire codebase.

Unlike tools that work at the file level, Cursor indexes your project, understands relationships between components, and maintains context across long conversations. It's like having a senior developer who never forgets your codebase structure.

1️⃣ Creating instructions and project context

🔧 Setting up `.cursorrules` for maximum impact

The .cursorrules file is where you define how Cursor should understand and work with your project. It's your project's "instruction manual" for AI assistance.

Basic structure:

# Project: Your Project Name

## Tech Stack
- Framework: React Native / Next.js / etc.
- Language: TypeScript
- State Management: Zustand / Redux / etc.
- Testing: Jest, React Testing Library

## Code Style
- Use functional components with hooks
- Prefer named exports over default exports
- Use TypeScript strict mode
- Follow eslint-config-prettier for formatting

## Conventions
- Components: PascalCase (UserProfile.tsx)
- Hooks: camelCase with 'use' prefix (useAuth.ts)
- Utilities: camelCase (formatDate.ts)
- Constants: UPPER_SNAKE_CASE (API_BASE_URL)

## Architecture Patterns
- Follow feature-based folder structure
- Keep components small and focused (max 200 lines)
- Use custom hooks for reusable logic
- Extract business logic to services

## Important Files
- @/lib/api.ts - API client configuration
- @/stores/authStore.ts - Authentication state
- @/hooks/useAuth.ts - Authentication hook

## When suggesting code:
1. Always include TypeScript types
2. Add error handling for async operations
3. Consider accessibility (a11y)
4. Write self-documenting code with clear variable names
5. Prefer composition over inheritance

💡 Pro tip: Start with a basic .cursorrules file, and refine it as you work. Cursor learns from how you edit its suggestions, so the file becomes more valuable over time.

📋 Project setup and context establishment

Before diving into code, establish context:

Chat with Cursor about your project structure:

"Can you analyze my project structure and explain the architecture?"
"What are the main patterns used in this codebase?"
"Show me examples of how state management is handled"

Index your codebase:
- Use Cursor's codebase indexing (if available in your plan)
- This helps Cursor understand file relationships and dependencies
- This allows Cursor to suggest imports and understand your project's conventions

Provide context in conversations:

I'm working on a React Native app with:
- Zustand for state management
- React Navigation for routing
- TypeScript strict mode
- Following the patterns in @/screens/ProfileScreen.tsx

💬 Comment: The more context you provide upfront, the better Cursor's suggestions will be. Think of it as briefing a new team member—they perform better when they understand the project's goals and constraints.

2️⃣ Creating scripts to automate processes

🤖 Generating automation scripts with Cursor

Cursor excels at understanding your project structure and generating scripts that fit your specific needs. Here's how to leverage it:

Example 1: Build and deployment scripts

# Ask Cursor:
"Create a script to build my React Native app for both iOS and Android, 
run tests, and deploy to staging if tests pass"

Cursor might generate:

#!/bin/bash
# build-and-deploy.sh

set -e

echo "🚀 Starting build and deployment process..."

# Run tests
echo "📝 Running tests..."
npm test -- --coverage

if [ $? -ne 0 ]; then
    echo "❌ Tests failed. Aborting deployment."
    exit 1
fi

# Build iOS
echo "🍎 Building iOS..."
cd ios && pod install && cd ..
npx react-native run-ios --configuration Release

# Build Android
echo "🤖 Building Android..."
npx react-native run-android --variant=release

# Deploy to staging (example)
echo "🚢 Deploying to staging..."
# Add your deployment command here

echo "✅ Build and deployment complete!"

Example 2: Code quality automation

# Ask Cursor:
"Create a pre-commit hook that runs ESLint, Prettier, and type checking"

#!/bin/sh
# .git/hooks/pre-commit

# Run ESLint
npm run lint
if [ $? -ne 0 ]; then
    echo "❌ ESLint checks failed"
    exit 1
fi

# Run Prettier
npm run format:check
if [ $? -ne 0 ]; then
    echo "❌ Code formatting issues found"
    exit 1
fi

# Type check
npm run type-check
if [ $? -ne 0 ]; then
    echo "❌ TypeScript errors found"
    exit 1
fi

echo "✅ All checks passed!"

Example 3: Database migration scripts

# Ask Cursor:
"Create a script that generates a database migration file with a timestamp"

// scripts/create-migration.ts
import { writeFileSync, mkdirSync } from 'fs';
import { join } from 'path';

const migrationName = process.argv[2];
if (!migrationName) {
  console.error('Usage: npm run create-migration <migration-name>');
  process.exit(1);
}

const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, -5);
const filename = `${timestamp}_${migrationName}.ts`;
const migrationsDir = join(__dirname, '../src/db/migrations');

mkdirSync(migrationsDir, { recursive: true });

const template = `import { Kysely } from 'kysely';

export async function up(db: Kysely<any>): Promise<void> {
  // Migration code here
}

export async function down(db: Kysely<any>): Promise<void> {
  // Rollback code here
}
`;

writeFileSync(join(migrationsDir, filename), template);
console.log(`✅ Created migration: ${filename}`);

💡 Pro tip: When asking Cursor to generate scripts, be specific about your project structure and tools. Reference existing scripts in your project so Cursor can match your style and conventions.

3️⃣ Taking advantage of Plans and Agents

📋 Understanding Plans in Cursor

Plans in Cursor allow you to break down complex tasks into smaller, manageable steps. When you have a multi-file change or a feature that requires multiple components, Plans help organize the work.

How to use Plans:

Start a Plan:

"Create a Plan to add user authentication:
- Create auth store with Zustand
- Add login screen with form validation
- Create protected route wrapper
- Add logout functionality to profile screen"

Review the Plan:
- Cursor will break down your request into actionable steps
- Each step can reference files that need to be created or modified
- You can modify or reorder steps before execution
Execute step by step:
- Review each change before applying
- Test as you go
- Modify the plan if requirements change

Example Plan workflow:

Plan: Add dark mode support

Step 1: Create theme store (stores/themeStore.ts)
  - Define theme state (light/dark)
  - Add toggle function
  - Persist to AsyncStorage

Step 2: Update app root (App.tsx)
  - Wrap with ThemeProvider
  - Apply theme to root View

Step 3: Create theme utilities (lib/theme.ts)
  - Define color palettes
  - Export theme-aware color helpers

Step 4: Update components
  - Update ProfileScreen to use theme colors
  - Update Button component
  - Update SettingsScreen with toggle

💬 Comment: Plans are particularly powerful for refactoring. They help you think through the changes systematically and ensure nothing is missed when modifying multiple files.

🤖 Using Agent mode effectively

Agent mode takes Plans to the next level—it can make multiple changes across your codebase autonomously, making decisions about implementation details.

When to use Agent mode:

✅ Complex multi-file refactoring: Changing a pattern across the entire codebase
✅ Feature implementation: Building a feature that requires several new files
✅ Bug fixes that span multiple components: When a fix requires coordinated changes

When NOT to use Agent mode:

❌ Simple, single-file changes: Regular Composer mode is faster
❌ Changes requiring specific domain knowledge: You might need to review each step
❌ Experimental code: When you're still exploring solutions

Agent mode best practices:

Provide clear constraints:

"Use Agent mode to refactor all API calls to use the new API client pattern.
Don't modify test files. Follow the pattern in @/lib/api/examples.ts"

Review changes before accepting:
- Agent mode makes many changes at once
- Review the diff carefully
- Test after accepting changes
Use with version control:
- Commit frequently when using Agent mode
- Makes it easier to revert if something goes wrong

Example Agent task:

"Use Agent mode to add error boundaries to all screen components.
Each screen should have its own error boundary that logs errors
to Sentry and shows a user-friendly error message.
Follow the pattern in @/components/ErrorBoundary.tsx"

💡 Pro tip: Start with Plans for complex tasks, then graduate to Agent mode once you're comfortable with how Cursor handles multi-file changes. Always review Agent changes before committing.

4️⃣ Combining different models for more context

🧠 Multi-model strategies in Cursor

Cursor supports multiple AI models, each with different strengths. Understanding when and how to use them together can dramatically improve results.

Available models and their strengths:

Claude (Sonnet/Opus): Excellent for reasoning, complex logic, and understanding context
GPT-4: Great for creative solutions and diverse coding patterns
GPT-3.5: Faster responses for simpler tasks

Strategy 1: Context expansion

When working on a complex problem:

Start with Claude (better context understanding):

"Analyze this authentication flow and identify potential security issues"

Use GPT-4 for implementation ideas:

"Suggest three different approaches to implement two-factor authentication"

Switch back to Claude for detailed implementation:

"Implement approach #1 with TypeScript, following our project conventions"

Strategy 2: Model-specific tasks

Use Claude for:
- Code reviews and refactoring suggestions
- Understanding complex legacy code
- Architectural decisions
- Security auditing
Use GPT-4 for:
- Generating boilerplate code
- Creative problem-solving
- Exploring new patterns
- Writing documentation

Strategy 3: Context window management

When working with large codebases:

Start with a summary request to Claude:

"Summarize how state management works in this codebase"

Use that summary as context for specific tasks:

"Based on the state management pattern, create a new store for user preferences"

Reference specific files when needed:

"Create a similar component to @/components/UserCard.tsx but for ProductCard"

💡 Pro tip: Keep a chat history of important architectural decisions and model recommendations. Reference these in future conversations to maintain consistency.

5️⃣ Advanced tips and workflows

📚 Codebase indexing and understanding

Enable codebase indexing (if available in your plan):

Improves Cursor's understanding of your project structure
Makes imports and references more accurate
Helps with refactoring across large codebases

Make the most of indexing:

Keep your codebase organized:
- Clear folder structure
- Consistent naming conventions
- Well-documented entry points

Use file references:

"Update @/components/Button.tsx to match the style in @/components/Card.tsx"

Reference multiple files for context:

"Create a new screen that follows the patterns in:
- @/screens/ProfileScreen.tsx (navigation)
- @/screens/SettingsScreen.tsx (state management)
- @/components/Form.tsx (form handling)"

💬 Chat history management

Organize your conversations:

Start new chats for different features:
- Keep conversations focused
- Makes history easier to search
- Prevents context from getting too large
Pin important messages:
- Save architectural decisions
- Keep project conventions accessible
- Reference solutions to common problems
Archive completed conversations:
- Keep your chat list clean
- Important chats remain searchable

Efficient chat prompts:

✅ Good: "Create a login form component following the pattern in @/components/RegisterForm.tsx"
❌ Less effective: "Create a form"
✅ Good: "Refactor this function to handle errors like the API client in @/lib/api.ts"
❌ Less effective: "Fix errors in this code"

⌨️ Keyboard shortcuts and efficiency tips

Essential Cursor shortcuts:

Cmd+K (Mac) / Ctrl+K (Windows/Linux): Open Composer
Cmd+L (Mac) / Ctrl+L (Windows/Linux): Chat with codebase
Cmd+I (Mac) / Ctrl+I (Windows/Linux): Inline edit
Tab: Accept suggestion
Esc: Dismiss suggestion

Workflow optimization:

Use inline edits for quick changes:
- Select code → Cmd+I → describe change
Use Composer for multi-file changes:
- Cmd+K → describe what you want → review plan → apply
Use Chat for exploration:
- Cmd+L → ask questions about codebase
- Use for understanding before making changes

Power user tips:

Multi-cursor editing: Select multiple instances → Edit in Cursor chat → Apply to all
Regex search: Use Cursor to help write regex patterns for find/replace
Code generation from comments: Write detailed comments → Let Cursor implement

🤖 GitHub Copilot: AI pair programming

GitHub Copilot is your real-time coding assistant, offering suggestions, refactoring help, and documentation as you type.

Copilot Tips for Maximum Productivity

📝 Write clear comments and function signatures

Copilot uses your comments and code context to generate better suggestions.

Example:

// Calculate the total price including tax and discounts
// @param basePrice - The base price before tax
// @param taxRate - Tax rate as decimal (e.g., 0.08 for 8%)
// @param discountPercent - Discount percentage (e.g., 10 for 10%)
// @returns The final price rounded to 2 decimal places
function calculateFinalPrice(
  basePrice: number,
  taxRate: number,
  discountPercent: number
): number {
  // Copilot will understand the intent and generate the calculation
}

🔄 Use Copilot for repetitive tasks

Generate boilerplate code, tests, or API calls quickly.

Prompt:

// Write a Jest test for this function

Example:

// User selects this code:
function formatCurrency(amount: number, currency: string): string {
  return new Intl.NumberFormat('en-US', {
    style: 'currency',
    currency: currency,
  }).format(amount);
}

// Then types: // Write a Jest test for this function
// Copilot generates:
describe('formatCurrency', () => {
  it('should format USD currency correctly', () => {
    expect(formatCurrency(100, 'USD')).toBe('$100.00');
  });

  it('should format EUR currency correctly', () => {
    expect(formatCurrency(50, 'EUR')).toBe('€50.00');
  });

  it('should handle decimal amounts', () => {
    expect(formatCurrency(99.99, 'USD')).toBe('$99.99');
  });
});

🛠️ Refactor with Copilot

Select code blocks and ask Copilot to optimize, add error handling, or convert to TypeScript.

Example workflow:

Select the function you want to refactor
Use Copilot Chat (Cmd+I in VS Code) or inline suggestions
Type: "Add error handling and convert to async/await"

Before:

function fetchUserData(userId) {
  return fetch(`/api/users/${userId}`)
    .then(res => res.json())
    .then(data => data);
}

After Copilot suggestion:

async function fetchUserData(userId: number): Promise<User> {
  try {
    const response = await fetch(`/api/users/${userId}`);
    if (!response.ok) {
      throw new Error(`Failed to fetch user: ${response.statusText}`);
    }
    const data = await response.json();
    return data;
  } catch (error) {
    console.error('Error fetching user data:', error);
    throw error;
  }
}

📚 Document as you code

Add docstrings or comments, and let Copilot auto-complete them.

Example:

// Just type the function:
function debounce<T extends (...args: any[]) => any>(
  func: T,
  wait: number
): (...args: Parameters<T>) => void {
  let timeout: NodeJS.Timeout;
  return function (this: any, ...args: Parameters<T>) {
    clearTimeout(timeout);
    timeout = setTimeout(() => func.apply(this, args), wait);
  };
}

// Then add a comment above and Copilot auto-generates documentation:
/**
 * Creates a debounced version of the provided function.
 * The debounced function will delay invoking func until after wait milliseconds
 * have elapsed since the last time the debounced function was invoked.
 *
 * @template T - The type of the function to debounce
 * @param func - The function to debounce
 * @param wait - The number of milliseconds to delay
 * @returns A debounced version of the function
 */

🗂️ Multi-file changes

Use Copilot's chat feature to refactor across multiple files.

Example:

// In Copilot Chat:
"Refactor all API calls to use the new axios instance from @/lib/api/axios.ts"

When you run multi-file refactors, Copilot's context awareness across open tabs helps ensure consistency.

Copilot can:

Identify all API calls across your project
Update imports
Refactor fetch calls to use axios
Maintain consistent error handling

⚡ Advanced Copilot Workflows

🔄 Multi-file Refactoring

Copilot Chat can help refactor patterns across multiple files. For example:

"Update all API calls to use the new error handling pattern from @/lib/api.ts"

Copilot will:

Find all relevant usages
Suggest consistent changes
Help you review diffs before applying

Example workflow:

Open Copilot Chat (Cmd+L or Ctrl+L in VS Code)
Reference the pattern file: "Update all authentication middleware to match @/middleware/auth.ts"
Review the suggested changes file by file
Apply changes incrementally, testing as you go

🧪 Test Generation

Copilot can generate unit, integration, and even E2E test skeletons. Just prompt:

"Write integration tests for the user authentication flow"

Example prompts:

"Generate unit tests for this React hook with edge cases"

"Create E2E tests for the checkout flow using Playwright"

"Write test cases for error handling in this API route"

Copilot understands your testing framework (Jest, Vitest, Playwright, etc.) and generates contextually appropriate test code.

📝 Documentation and Comments

Copilot can auto-complete JSDoc/TSDoc comments, generate README sections, and even draft changelogs from commit messages.

Documentation examples:

// Type: /**
// Copilot generates:
/**
 * Calculates the total price including taxes and discounts.
 * @param basePrice - The base price before any adjustments
 * @param taxRate - Tax rate as a decimal (e.g., 0.08 for 8%)
 * @param discountPercent - Discount percentage (e.g., 10 for 10%)
 * @returns The final price rounded to 2 decimal places
 * @throws {Error} If basePrice is negative
 */

README generation:

"Generate a README section explaining the installation process"

Changelog generation:

"Create a changelog entry for these commits: [paste commits]"

💡 Copilot Chat Tips

Use Copilot Chat for code explanations, debugging, and exploring alternatives.

Effective prompts:

Code explanations:

"Explain how this React component manages state and side effects"

Debugging:

"Why is this component re-rendering on every keystroke?"

Performance improvements:

"Suggest performance improvements for this database query"

Alternative approaches:

"Show me three different ways to implement this feature"

Best practices for Copilot Chat:

Provide file references and context for better suggestions
Use @filename to reference specific files in your project
Break complex requests into smaller, focused questions
Ask follow-up questions to refine suggestions

🚦 Limitations & Best Practices

Copilot works best with:

Clear, modular code structure
Descriptive comments and variable names
Well-defined function signatures
Consistent coding patterns

Always remember to:

✅ Review Copilot's suggestions for correctness and security
✅ Test generated code before committing
✅ Understand what the code does, don't just accept blindly
✅ Use Copilot as a starting point, then refine

For domain-specific logic:

Supplement Copilot with manual review
Use other AI tools (like Cursor or ChatGPT) for complex architectural decisions
Don't rely solely on Copilot for business-critical logic

🛠️ Troubleshooting

If Copilot suggestions are off-topic:

Add more context in comments or function names
Split your code into smaller, more focused functions
Provide examples of the desired pattern

If suggestions are repetitive or unhelpful:

Use Copilot Chat to clarify intent: "I need help with [specific problem]"
Ask for step-by-step guidance: "Walk me through implementing [feature]"
Reference similar code: "Make this work like @/components/SimilarComponent.tsx"

If Copilot seems to misunderstand your codebase:

Add more descriptive comments
Improve function and variable naming
Consider creating a small example file that demonstrates the pattern you want

💡 Pro tip: If Copilot's suggestions aren't accurate, add more context or split your code into smaller functions. Copilot works better with well-structured, modular code.

🧠 ChatGPT: Turning prompts into engineering power

Why ChatGPT matters in your workflow

ChatGPT (or the OpenAI API) is your general-purpose engineer—a brainstorming partner, documentation writer, and automation engine all in one.

Unlike Copilot or Cursor, it's not tied to your local codebase—but with the right instructions, it can reason about architecture, tooling, and product context.

1️⃣ Setting up effective ChatGPT instructions

Before jumping into prompts, give ChatGPT the context it needs to act like a senior engineer in your project.

Example system prompt for a development session:

You are a senior software engineer helping with a React Native mobile application.

Project Context:
- Tech Stack: React Native, TypeScript, Zustand, React Navigation
- Code Style: Functional components, named exports, strict TypeScript
- Architecture: Feature-based folder structure
- Testing: Jest, React Testing Library, Detox for E2E

Your role:
1. Suggest solutions that follow our established patterns
2. Prioritize code quality, performance, and maintainability
3. Always include error handling and edge cases
4. Provide explanations for architectural decisions

When suggesting code:
- Use TypeScript with proper types
- Follow the patterns from our existing codebase
- Include relevant tests
- Consider React Native best practices

💡 Pro tip: Save this as a reusable "Custom GPT" or system prompt. It helps ChatGPT maintain consistency across conversations.

2️⃣ Practical ChatGPT instructions for developers

Here are some real prompts you can use daily to accelerate your workflow:

🧱 Architecture & setup

"Analyze my folder structure and suggest improvements for scalability in a React Native monorepo."

"Generate a dependency audit and suggest safe upgrades for Expo SDK 52 and React Native 0.81."

⚙️ Development automation

"Create a Bash script to build and deploy my React Native app using EAS CLI, with Slack notification when complete."

"Generate a GitHub Actions workflow that runs ESLint, Jest, and type-checking before merging to main."

🧪 Testing & stability

"Write an end-to-end test in Detox for the onboarding flow."

"Create a script to monitor Sentry issues weekly and post summary in Slack."

📚 Documentation & onboarding

"Generate concise onboarding documentation for new React Native developers joining our team."

"Summarize key architectural decisions from this README into a Notion-friendly format."

🔍 Refactoring & performance

"Review this function and optimize re-renders using React.memo or useCallback."

"Refactor this component to follow the pattern used in @/components/UserCard.tsx."

3️⃣ Advanced ChatGPT techniques for developers

🧠 Combine reasoning + creativity

Use GPT-4 Turbo or GPT-4 for deep architectural reasoning, and switch to GPT-3.5 for fast code generation.

Example workflow:

Step 1 (GPT-4):

"Design a clean architecture for offline sync in a React Native app using SQLite."

Step 2 (GPT-3.5):

"Generate TypeScript models and service layer from that design."

🧩 Chain tasks with context

"Summarize the key goals from our previous chat about app stability, then generate a checklist for crash monitoring before next release."

🛠️ Integrate with your dev stack

Use ChatGPT to generate CLI tools via OpenAI API
Connect to your issue tracker (e.g., Jira or Linear) to draft tasks
Use function-calling mode to run code audits or deploy pipelines

4️⃣ ChatGPT in your daily workflow

| Task | Example Prompt | |------|----------------| | Feature design | "Create a Plan for adding biometric login with Expo LocalAuthentication and Zustand." | | Debugging | "Explain why this React Native component causes a memory leak." | | Documentation | "Write a changelog for version 2.12.0 based on these commits." | | Mentorship | "Explain React Navigation's stack behavior like I'm mentoring a junior dev." | | Code review | "Review this PR diff and identify potential regressions or unhandled states." |

🧭 Tip: Keep one "AI Engineering Log" file per project (Markdown or Notion). Save useful prompts and decisions for future reference.

Once you're comfortable with prompt-based workflows, you can extend ChatGPT's power even further through customization and automation.

5️⃣ Custom GPTs & API integration

Once you've mastered prompts, take it further by creating Custom GPTs or automating workflows via the OpenAI API.

🧩 Create your own project-aware GPT

Inside ChatGPT's "Explore GPTs" tab:

Click Create a GPT
Paste your system instructions (the same you use at the start of this article)
Upload:
- README.md
- Folder structure snapshot (tree -L 2)
- ESLint or .cursorrules file
Define your tone and level of verbosity (e.g., "concise, production-ready TypeScript")

Now you have a private AI teammate that remembers your conventions across sessions—perfect for onboarding or long-term consistency.

⚙️ Automate with the OpenAI API

Use the API to connect ChatGPT to your internal dev tools:

import OpenAI from "openai";

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function generateReleaseNotes(commits: string[]) {
  const prompt = `
  Summarize these commits into release notes:
  ${commits.join("\n")}
  Format in Markdown with sections for Features, Fixes, and Improvements.
  `;

  const res = await openai.chat.completions.create({
    model: "gpt-4-turbo",
    messages: [{ role: "user", content: prompt }],
  });

  return res.choices[0].message.content;
}

Use similar patterns for:

CI/CD notifications
Automated code reviews
Slack summaries after deployments
Internal documentation generation

💡 Pro tip: Treat the OpenAI API like any other internal service—version its prompts, monitor usage, and cache results when possible. Teams at scale often version these GPTs like internal tools — tagging configurations by project or environment.

⚖️ Choosing the right AI for each task

| Tool | Best For | Strength | |------|----------|----------| | Cursor | Context-aware refactors & feature Plans | Deep project understanding | | Copilot | Inline completions | Fast coding flow | | ChatGPT | Architecture, documentation, automation | Strategic reasoning |

Combining tools effectively

Recommended workflow:

Start with ChatGPT: Explore solutions and understand the problem
```
"How should I structure a React Native authentication flow?"
```

Move to Cursor: Implement with full context

"Implement the authentication flow we discussed, following our project patterns"

Use Copilot: Fill in details and complete boilerplate
- Auto-complete repetitive code
- Generate test cases
- Complete function implementations

Example multi-tool workflow:

1. ChatGPT: "What are best practices for error handling in React Native?"
2. Cursor: "Implement global error handling based on these practices"
3. Copilot: Auto-complete error handler functions as you type
4. Cursor: "Review and improve error handling implementation"

Workflow recommendations

Daily development:

Use Copilot for inline completions
Use Cursor for complex changes and refactoring
Use ChatGPT for learning and exploration

Feature development:

Start with ChatGPT or Cursor Chat to plan the feature
Use Cursor Plans to break down implementation
Use Copilot to speed up coding
Use Cursor Agent for coordinated multi-file changes

Code review:

Use Cursor to analyze pull requests
Use ChatGPT for architectural feedback
Combine manual review with AI suggestions

🚀 Next steps

Week 1: Set up the basics

Install Cursor and configure .cursorrules
Set up GitHub Copilot in your editor
Create an OpenAI account for experimentation

Week 2: Establish workflows

Try using Plans in Cursor for a feature
Practice writing effective prompts
Start building your project's AI documentation

Week 3: Optimize and automate

Create scripts using AI assistance
Set up automation workflows
Share learnings with your team

Month 2+: Advanced techniques

Experiment with Agent mode
Build custom GPTs for your domain
Integrate AI tools into CI/CD
Measure and optimize your AI-assisted workflow

💬 Final thought

AI won't replace developers — but developers who master AI workflows will replace those who don't.

However, there's no single magical tool that solves everything. The real power comes from combining tools and processes strategically. Cursor for context-aware refactoring, Copilot for inline completion, ChatGPT for architectural thinking—each serves a unique purpose in your workflow.

The goal isn't to find the "perfect" AI tool. It's to build a systematic approach that lets you:

Move faster without sacrificing code quality
Deliver stable, production-ready features in record time
Scale your development process as your team grows

Build your AI muscle memory: prompt, test, refine, repeat. Experiment with combinations, measure what works for your team, and iterate on your process. The developers who will lead are those who treat AI tools as part of a broader engineering discipline—not as shortcuts, but as force multipliers.

💬 If you found this helpful, share it with your team or comment which AI workflow you're experimenting with — learning from others is how this ecosystem evolves.

#AI #ChatGPT #Cursor #GitHubCopilot #ReactNative #SoftwareEngineering #Productivity #DevTools

From coding to shipping: How AI is reshaping development workflows

🎯 What you'll learn

🧩 Cursor: Context-aware coding at scale

Why Cursor stands out

1️⃣ Creating instructions and project context

🔧 Setting up .cursorrules for maximum impact

📋 Project setup and context establishment

2️⃣ Creating scripts to automate processes

🤖 Generating automation scripts with Cursor

3️⃣ Taking advantage of Plans and Agents

📋 Understanding Plans in Cursor

🤖 Using Agent mode effectively

4️⃣ Combining different models for more context

🧠 Multi-model strategies in Cursor

5️⃣ Advanced tips and workflows

📚 Codebase indexing and understanding

💬 Chat history management

⌨️ Keyboard shortcuts and efficiency tips

🤖 GitHub Copilot: AI pair programming

Copilot Tips for Maximum Productivity

📝 Write clear comments and function signatures

🔄 Use Copilot for repetitive tasks

🛠️ Refactor with Copilot

📚 Document as you code

🗂️ Multi-file changes

⚡ Advanced Copilot Workflows

🔄 Multi-file Refactoring

🧪 Test Generation

📝 Documentation and Comments

💡 Copilot Chat Tips

🚦 Limitations & Best Practices

🛠️ Troubleshooting

🧠 ChatGPT: Turning prompts into engineering power

Why ChatGPT matters in your workflow

1️⃣ Setting up effective ChatGPT instructions

2️⃣ Practical ChatGPT instructions for developers

🧱 Architecture & setup

⚙️ Development automation

🧪 Testing & stability

📚 Documentation & onboarding

🔍 Refactoring & performance

3️⃣ Advanced ChatGPT techniques for developers

🧠 Combine reasoning + creativity

🧩 Chain tasks with context

🛠️ Integrate with your dev stack

4️⃣ ChatGPT in your daily workflow

5️⃣ Custom GPTs & API integration

🧩 Create your own project-aware GPT

⚙️ Automate with the OpenAI API

⚖️ Choosing the right AI for each task

Combining tools effectively

Workflow recommendations

🚀 Next steps

💬 Final thought

🔧 Setting up `.cursorrules` for maximum impact