How to Use AI Coding Tools: Complete Guide 2026

How to Use AI Coding Tools

AI coding tools transform software development by providing intelligent code generation, completion, and assistance. This guide covers practical workflows for different types of AI coding tools, from simple code completion to autonomous agent-based development.

Getting Started: Choosing Your Tool

Before diving into workflows, choose the right tool for your needs:

• Code Completion Extensions: GitHub Copilot, Tabnine - Best for gradual adoption, works in existing IDEs
• AI-Native Editors: Cursor - Best for deep AI integration with repository-wide context
• Agent-First IDEs: Google Antigravity - Best for autonomous multi-file refactoring and parallel task execution
• Cloud Platforms: Replit, Bolt.new - Best for collaborative coding and rapid prototyping
• Vibe Coding Platforms: Lovable.dev, v0.dev - Best for non-technical users building applications

Workflow 1: Code Completion Extensions (GitHub Copilot)

Setup

1. Install GitHub Copilot extension in your IDE (VS Code, JetBrains, etc.)
2. Sign in with your GitHub account
3. Start typing code - Copilot activates automatically

Basic Usage

Accepting Suggestions: When Copilot suggests code, press Tab to accept, or Ctrl+→ (Windows/Linux) / Cmd+→ (Mac) to accept word-by-word.

Seeing Alternatives: Press Alt+] (Windows/Linux) or Option+] (Mac) to cycle through multiple suggestions for the same context.

Triggering Suggestions: Write a comment describing what you want, then press Enter. Copilot generates code based on your description.

Advanced Techniques

• Function Generation: Write a function signature and docstring. Copilot generates the implementation.
• Test Generation: Write a test function name and description. Copilot generates comprehensive test cases.
• Refactoring: Select code and describe the refactoring you want. Copilot suggests changes.

cursor">Workflow 2: AI-Native Editor (Cursor)

Initial Setup

1. Download and install Cursor from cursor.sh
2. Open your project folder - Cursor automatically indexes the codebase
3. Configure AI model preferences (GPT-4, Claude, etc.) in settings
4. Set up API keys for your preferred models

Chat Interface

Press Cmd+L (Mac) or Ctrl+L (Windows/Linux) to open the chat. Ask questions about your codebase:

• "How does the authentication system work?"
• "Where is the user profile data stored?"
• "Generate a function to validate email addresses"

Cursor uses full repository context, so answers are accurate to your specific codebase.

Composer Mode (Multi-File Edits)

1. Press Cmd+I (Mac) or Ctrl+I (Windows/Linux) to open Composer
2. Describe the change you want across multiple files
3. Review the proposed changes in the diff view
4. Accept or modify individual file changes

Example: "Add error handling to all API endpoints. Create a centralized error handler in utils/errors.js and update all routes to use it."

Codebase Indexing

Cursor indexes your codebase on first open. For large projects:

• Let indexing complete before using chat features
• Add .cursorignore to exclude unnecessary files (node_modules, build outputs)
• Indexing improves context accuracy for code generation

Workflow 3: Agent-First IDE (Google Antigravity)

Understanding Agent Paradigm

Antigravity uses autonomous AI agents that can plan, execute, and validate code changes. You delegate tasks, and agents work independently.

Editor View Workflow

1. Open Antigravity and load your project
2. Use the agent sidebar to describe tasks
3. Agents analyze codebase and create implementation plans
4. Review "Artifacts" (task lists, screenshots) before execution
5. Approve or modify agent plans
6. Agents execute changes and provide results

Manager View (Parallel Agents)

1. Switch to Manager View (control center)
2. Create multiple agents for different tasks
3. Assign agents to different workspaces
4. Monitor agent progress across parallel tasks
5. Review and merge agent-generated changes

Example Workflow: "Agent 1: Refactor authentication module. Agent 2: Add unit tests for user service. Agent 3: Update API documentation."

Agent Artifacts

Agents generate verifiable deliverables:

• Task Lists: Breakdown of planned changes
• Implementation Plans: Detailed steps before execution
• Screenshots: Visual proof of changes
• Browser Recordings: Demonstrations of functionality

Review artifacts before approving agent execution to ensure changes match your intent.

replit">Workflow 4: Cloud-Based Development (Replit)

Getting Started

1. Create a Replit account at replit.com
2. Start a new project or import from GitHub
3. Replit Agent activates automatically in the IDE

Using Replit Agent

Click the Agent icon or press Cmd+K to open Agent chat. Describe what you want:

• "Add a login page with email and password fields"
• "Create an API endpoint to fetch user data"
• "Fix the bug in the authentication middleware"

Agent generates code, explains changes, and can execute terminal commands.

Collaborative Features

• Share projects via URL - no setup required
• Multiple developers can work simultaneously
• Agent suggestions are visible to all collaborators

Workflow 5: Vibe Coding (Lovable.dev)

Building Applications with Natural Language

1. Go to lovable.dev and create a new project
2. Describe your application idea in plain English
3. Lovable generates a complete application (frontend, backend, database)
4. Iterate by describing changes or improvements

Example: "Create a todo app with user authentication. Users can create, edit, and delete todos. Todos are stored in a database and persist across sessions."

Knowledge Files

Lovable maintains project context through "Knowledge Files" - documents that describe your application's architecture, features, and requirements. Update these files to guide future AI-generated code.

Iterative Refinement

After initial generation, describe improvements:

• "Add dark mode toggle"
• "Make the UI more modern with rounded corners and better spacing"
• "Add email notifications when todos are completed"

Best Practices for AI Coding

1. Write Clear, Specific Prompts

Good prompts are specific and include context:

• ❌ "Add authentication"
• ✅ "Add JWT-based authentication. Create a /login endpoint that accepts email and password, validates credentials against the users table, and returns a JWT token. Store tokens in httpOnly cookies."

2. Review Generated Code

Always review AI-generated code before committing:

• Check for security vulnerabilities
• Verify it follows your project's patterns
• Test functionality thoroughly
• Ensure error handling is appropriate

3. Use AI for Repetitive Tasks

AI excels at repetitive, pattern-based tasks:

• Generating boilerplate code
• Creating test cases
• Writing documentation
• Refactoring similar code blocks

4. Leverage Context

Tools like Cursor work best when they understand your full codebase:

• Let indexing complete before heavy usage
• Reference existing code patterns in prompts
• Use chat to understand codebase before generating new code

5. Iterate and Refine

AI coding is iterative:

• Start with a basic prompt
• Review generated code
• Refine prompts based on results
• Use follow-up questions to improve output

6. Understand Limitations

AI coding tools have limitations:

• May not understand complex business logic
• Can generate code with security vulnerabilities
• May not follow all best practices
• Require human oversight for production code

Common Use Cases

Generating API Endpoints

Describe the endpoint structure, request/response formats, and business logic. AI generates complete route handlers with validation and error handling.

Creating React Components

Describe the component's purpose, props, and UI requirements. AI generates complete components with proper TypeScript types and styling.

Writing Tests

Describe what to test and expected behaviors. AI generates comprehensive test suites covering edge cases and error scenarios.

Refactoring Legacy Code

Select code blocks and describe desired improvements. AI suggests refactored versions that maintain functionality while improving structure.

Debugging

Paste error messages and relevant code. AI analyzes the issue and suggests fixes based on common patterns and best practices.

Integration with Existing Workflows

Version Control

AI-generated code should follow your Git workflow:

• Review changes in staging area before committing
• Write meaningful commit messages
• Use AI to generate commit message suggestions

Code Review

Treat AI-generated code like human-written code:

• Review for correctness and security
• Ensure it follows team standards
• Verify tests pass
• Check for performance implications

CI/CD Pipelines

AI-generated code should pass all CI checks:

• Linting and formatting
• Type checking
• Unit and integration tests
• Security scans

Troubleshooting

AI Not Understanding Context

If suggestions seem off-topic:

• Ensure codebase indexing is complete (Cursor)
• Provide more context in your prompt
• Reference specific files or functions
• Use chat to explain codebase structure first

Generated Code Doesn't Match Style

To improve style matching:

• Show examples of your code style in prompts
• Configure formatting rules in your IDE
• Use linters to enforce style after generation
• Train AI on your codebase patterns over time

Performance Issues

If tools are slow:

• Exclude large files from indexing (.cursorignore)
• Use faster models for simple tasks (GPT-3.5 vs GPT-4)
• Limit context window size in settings
• Consider local models for sensitive code

Conclusion

AI coding tools accelerate development by handling repetitive tasks, generating boilerplate code, and providing intelligent assistance. The key to effective use is understanding each tool's strengths, writing clear prompts, and always reviewing generated code.

Start with code completion extensions for gradual adoption, then explore AI-native editors for deeper integration. Agent-based IDEs offer the most automation but require understanding new workflows. Choose the tool that fits your development style and project needs.

Explore our curated selection of AI coding tools to find the right solution. For foundational knowledge, see our guide on what AI coding is.