AI Tools
What to Use & When
A Comprehensive Developer's Guide
Overview
This comprehensive seminar is designed to help developers navigate the rapidly evolving landscape of AI-powered development tools.
What You'll Learn
- Strategic AI Tool Selection - Understand when and why to use different AI models and platforms
- Hands-On Tool Comparison - Compare popular AI coding assistants and their unique strengths
- Practical Implementation - Get step-by-step guidance on customizing GitHub Copilot
- Real-World Applications - See how AI tools integrate into modern development workflows
Who This Is For
- Developers looking to boost productivity with AI assistance
- Team leads evaluating AI tools for their teams
- Anyone curious about the current state of AI in software development
Agenda
AI is a game changer
The AI Revolution in Development
AI augments developers with superpowers
Instead of replacing them
Recent model comparisons
Understanding the AI Model Landscape
AI Coding IDEs
The IDE Integration Landscape
Customize GitHub Copilot in VS Code
Advanced Customization Features
AI is a game changer
The AI Revolution in Development
The AI Revolution in Development
Over the past year, AI has evolved from simple chat interfaces to become deeply integrated into developer workflows.
What started as experimental tools has transformed into:
- Essential productivity enhancers
- Integrated development companions
- Code quality improvement tools
- Learning acceleration platforms
From Fear to Empowerment
Initial Concerns
AI replacing developers
Reality
Augmentation
Accelerate development cycles
Generate boilerplate code and suggest implementations
Reduce cognitive load
Handle repetitive tasks and documentation
Improve code quality
Intelligent suggestions and error detection
Enable faster learning
Instant explanations and examples
Best Practices for AI-Enhanced Development
📝 Meaningful Documentation
Detailed docstrings and comments help AI understand your code's intent
🏷️ Descriptive Naming
Clear function and variable names provide crucial context for AI
🏗️ Code Structure
Well-organized, modular code helps AI understand patterns
💭 Context-Rich Comments
Explaining the "why" helps AI make better decisions
AI augments developers with superpowers
Instead of replacing them
Two Categories of AI Coding Tools
🤖 Fully Independent Agents
What they are: Complete code generation from natural language prompts
Examples: Bolt, Replit Agent, CodeSandbox AI
Best for: Rapid prototyping, simple applications, non-developers
Limitations: Less control over implementation details
👥 Semi-Independent Tools
What they are: AI assistants that work alongside your existing workflow
Examples: GitHub Copilot, Cursor, v0 (Vercel), Codeium
Best for: Professional development, complex projects
Advantages: Full control over code, iterative development
Recent model comparisons
Understanding the AI Model Landscape
Model Selection by Use Case
⚖️ Balance: Cost vs Performance
- GPT-4o - General-purpose with strong coding
- Claude 3.5 Sonnet - Great for code analysis
⚡ Fast & Low-Cost
- GPT-4o mini - Quick simple suggestions
- Claude 3.5 Haiku - Efficient documentation
🧠 Deep Reasoning
- o1 - Complex problem-solving
- Claude 3.5 Sonnet - Code review & architecture
🎯 Multimodal & Real-time
- Gemini 2.0 Flash - Image/diagram understanding
- GPT-4o - UI/UX development
Cost Considerations
Typical Cost Ranges (per 1M tokens)
| GPT-4o | $2.50-$10.00 |
| GPT-4o mini | $0.15-$0.60 |
| Claude 3.5 Sonnet | $3.00-$15.00 |
| Claude 3.5 Haiku | $0.25-$1.25 |
💡 Cost Optimization Tips
- Use cheaper models for simple tasks
- Reserve premium models for complex reasoning
- Implement token limits
- Monitor usage patterns
AI Coding IDEs
The IDE Integration Landscape
Popular AI Coding Platforms
🐙 GitHub Copilot
Integration: Plugin-based for existing IDEs
Best for: Enterprise environments, established workflows
Strengths: Mature ecosystem, strong enterprise support
🎯 Cursor
Integration: Standalone IDE on VS Code foundation
Best for: Cutting-edge AI features, AI-first development
Strengths: Native AI integration, composer mode
⚡ v0 by Vercel
Integration: Web-based editor with GitHub sync
Best for: Frontend development, rapid UI prototyping
Strengths: Component generation, live preview
Pricing & Security Comparison
| Platform | Monthly | Key Security Features |
|---|---|---|
| GitHub Copilot | $10-$19/month | SOC 2 certified, IP indemnification |
| Cursor | $20/month | Local processing, SOC 2 compliance |
| v0 by Vercel | $20/month | Code isolation, no training on code |
🔒 Security Best Practice: Use enterprise versions for sensitive projects and review your organization's data policies before adoption.
Customize GitHub Copilot in VS Code
Advanced Customization Features
Key Customization Features
📝 Instruction Files
.github/copilot-instructions.md
Project-wide coding standards, automatically applied to all chat requests
⚙️ Custom Settings
Configure specific instructions for different scenarios:
- Code generation
- Test generation
- Code review
- Commit messages
🎯 Prompt Files
Reusable .prompt.md files for common tasks
Support for variables and file references
Setup Steps
Enable Settings
Enable chat.promptFiles and github.copilot.chat.codeGeneration.useInstructionFiles
Create Instructions
Create .github/copilot-instructions.md for project-wide rules
Organize by Topic
Add specific .instructions.md files in .github/instructions/ folder
Configure VS Code
Configure custom instructions in VS Code settings
Key Takeaways
🤝 AI as a Development Partner
AI tools augment rather than replace developers
🎯 Strategic Tool Selection
Choose models based on specific use cases
⚡ Practical Implementation
Start with one tool and customize with clear instructions
🔒 Security & Compliance
Use enterprise versions and implement proper code review
Your Next Steps
🚀 Immediate Actions
- Try a free tier of GitHub Copilot or Cursor
- Set up basic customization with
.github/copilot-instructions.md - Establish team guidelines for AI tool usage
📈 Medium-term Goals
- Evaluate ROI and track productivity gains
- Expand usage across different development phases
- Build internal best practices and training
🌟 Long-term Strategy
- Stay informed about rapidly evolving AI tools
- Scale adoption from individual to team-wide
- Contribute learnings back to the community
Thank You!
Questions & Discussion
Remember: The goal isn't to become dependent on AI, but to become more effective developers by leveraging AI as a powerful tool in your development arsenal.