AI Tools – What to Use & When

Overview

This comprehensive seminar is designed to help developers navigate the rapidly evolving landscape of AI-powered development tools. You'll learn how to strategically choose and implement AI tools that enhance your productivity without replacing your expertise.

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 for your projects
• 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

Key Takeaways

By the end of this session, you'll have a clear framework for choosing AI tools, practical experience with customization, and actionable strategies to implement in your daily development work.

Agenda

1. AI is a game changer - The AI Revolution in Development
2. AI augments developers with superpowers instead of replacing them
3. Recent model comparisons - Understanding the AI Model Landscape
4. AI Coding IDEs - The IDE Integration Landscape
5. Customize GitHub Copilot in VS Code - Advanced Customization Features

1) AI is a game changer

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 that are reshaping how we write, review, and deploy code.

From Fear to Empowerment

While there were initial concerns about AI replacing developers, the reality has proven quite different. Instead of replacement, we're seeing augmentation - AI tools are becoming powerful coding companions that:

• Accelerate development cycles by generating boilerplate code and suggesting implementations
• Reduce cognitive load by handling repetitive tasks and documentation
• Improve code quality through intelligent suggestions and error detection
• Enable faster learning by providing instant explanations and examples

The New Developer Landscape

Today's developers who effectively leverage AI tools report significant productivity gains, with some studies showing 20-40% faster completion times for common coding tasks. The key is understanding how to work with AI rather than being replaced by it.

Best Practices for AI-Enhanced Development

With AI as your coding companion, traditional best practices become even more critical:

• Meaningful Documentation - Detailed docstrings and comments help AI understand your code's intent and generate better suggestions
• Descriptive Naming - Clear function and variable names provide crucial context that AI uses to generate relevant code
• Code Structure - Well-organized, modular code helps AI understand patterns and maintain consistency
• Context-Rich Comments - Explaining the "why" behind your code helps AI make better decisions in similar situations

Remember: Good code practices don't just help humans - they're essential for effective AI collaboration.

2) AI augments developers with superpowers instead of replacing them

The Evolution of AI Integration

Modern LLMs have moved beyond standalone chat interfaces to become deeply integrated into development environments. This integration represents a fundamental shift in how developers interact with AI assistance.

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, harder to customize

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, experienced developers
• Advantages: Full control over code, iterative development, integration with existing tools

Why Semi-Independent Tools Win for Developers

The second category is specifically designed for professional developers because it:

• Preserves developer expertise - You remain in control of architecture and design decisions
• Enables iterative refinement - Collaborate with AI to improve and customize code
• Integrates with existing workflows - Works with your preferred IDE, version control, and tools
• Maintains code quality - You can review, modify, and optimize AI suggestions
• Supports complex projects - Handles enterprise-level codebases and requirements

3) Recent model comparisons

Understanding the AI Model Landscape

There are many models available today, but they're not equally good for all tasks, so you need to choose them appropriately for your specific use case. Each model has unique strengths, costs, and performance characteristics.

Model Selection by Use Case

For balance between cost and performance:

• GPT-4o - Excellent general-purpose model with strong coding capabilities
• Claude 3.5 Sonnet - Great for code analysis and complex reasoning tasks

For fast, low-cost support for basic tasks:

• GPT-4o mini - Quick responses for simple code suggestions and explanations
• Claude 3.5 Haiku - Efficient for documentation and basic code generation

For deep reasoning or complex coding challenges:

• o1 - Specialized for complex problem-solving and mathematical reasoning
• GPT-4o - Robust performance across diverse coding scenarios
• Claude 3.5 Sonnet - Excellent for code review and architectural decisions

For multimodal inputs and real-time performance:

• Gemini 2.0 Flash - Fast processing with image/diagram understanding
• GPT-4o - Reliable multimodal capabilities for UI/UX development

Key Considerations When Choosing Models

• Cost vs Performance - Balance your budget with quality requirements
• Response Speed - Consider latency for real-time coding assistance
• Context Window - Larger contexts for complex codebases
• Specialized Capabilities - Some models excel at specific programming languages
• Integration Support - Ensure compatibility with your development tools
• Data Privacy - Consider where your code is processed and stored
• Enterprise Compliance - Ensure models meet your organization's security requirements

Cost Considerations for AI Models

Different AI models have varying cost structures that can significantly impact your development budget:

Pricing Factors

• Input tokens - Cost per token sent to the model (your prompts and code context)
• Output tokens - Cost per token generated by the model (responses and code)
• Model tier - Premium models (GPT-4o, Claude 3.5 Sonnet) cost more than basic models
• Volume discounts - Many providers offer reduced rates for high-volume usage

Typical Cost Ranges (per 1M tokens, approximate)

• GPT-4o: $2.50-$10.00 (input/output)
• GPT-4o mini: $0.15-$0.60 (input/output)
• Claude 3.5 Sonnet: $3.00-$15.00 (input/output)
• Claude 3.5 Haiku: $0.25-$1.25 (input/output)

Cost Optimization Tips

• Use cheaper models for simple tasks (documentation, basic code generation)
• Reserve premium models for complex reasoning and architecture decisions
• Implement token limits to control spending
• Monitor usage patterns to optimize model selection

4) AI Coding IDEs

The IDE Integration Landscape

AI has fundamentally transformed how we interact with development environments. Modern AI-powered IDEs offer everything from intelligent code completion to full conversational programming assistance, making them essential tools for modern development workflows.

Popular AI Coding Platforms

GitHub Copilot

• Integration: Plugin-based approach for existing IDEs
• Supported IDEs: VS Code, Visual Studio, JetBrains IDEs, Xcode, Neovim
• Strengths: Mature ecosystem, excellent code completion, strong enterprise support
• Best for: Teams already using established IDEs, enterprise environments
• Models: GPT-4o, Claude 3.5 Sonnet, o1, and more

Cursor

• Integration: Standalone IDE built on VS Code foundation
• Strengths: Native AI integration, advanced chat features, composer mode for multi-file edits
• Best for: Developers wanting cutting-edge AI features in a familiar environment
• Unique features: AI-first design, seamless context awareness across projects

v0 by Vercel

• Integration: Web-based editor with GitHub integration
• Strengths: Excellent for rapid UI prototyping, component generation, preview capabilities
• Best for: Frontend developers, UI/UX design iteration, quick prototypes
• Limitations: Primarily focused on web UI development

Choosing the Right AI IDE

Consider GitHub Copilot if:

• You're already invested in a specific IDE ecosystem
• You need enterprise-grade security and compliance
• You want gradual AI adoption without changing workflows

Consider Cursor if:

• You want the most advanced AI coding features
• You're comfortable with newer, rapidly evolving tools
• You prioritize AI-native development experience

Consider v0 if:

• You primarily build web interfaces and components
• You need quick prototyping and iteration capabilities
• You work extensively with React and modern frontend frameworks

Pricing Comparison

Platform	Monthly	Yearly	Features
GitHub Copilot	$10/month	$100/year	Individual plan, all supported IDEs
GitHub Copilot Business	$19/user/month	$228/user/year	Team management, enterprise features
Cursor	$20/month	$200/year	Pro plan with advanced AI features
v0 by Vercel	$20/month	$200/year	Pro plan with unlimited generations

Note: Prices are subject to change. Free tiers and student discounts may be available.

Security and Privacy Considerations

GitHub Copilot Security Features:

• Enterprise-grade security - SOC 2 Type II certified
• Code privacy - Your code is not used to train public models
• Content filtering - Blocks suggestions matching public code
• Audit logs - Track usage and compliance for organizations
• IP indemnification - Legal protection for enterprise customers

Cursor Security Features:

• Local processing - Some features run locally for enhanced privacy
• Data encryption - All communications encrypted in transit
• No training on your code - Explicit privacy commitments
• SOC 2 compliance - Enterprise security standards

v0 Security Features:

• Vercel infrastructure - Built on enterprise-grade platform
• Code isolation - Projects are isolated and secure
• GitHub integration - Leverages GitHub's security model
• No code training - Generated code remains private

Best Practices for AI Tool Security:

• Review your organization's data policies before adoption
• Use enterprise versions for sensitive projects
• Avoid including secrets or sensitive data in prompts
• Regularly audit AI tool permissions and access
• Consider on-premises or hybrid solutions for highly sensitive work

Pricing Comparison (as of 2025)

GitHub Copilot

• Individual: $10/month or $100/year
• Business: $19/user/month
• Enterprise: $39/user/month
• Free tier: Available for students, teachers, and open source maintainers

Cursor

• Free: Basic features with limited AI requests
• Pro: $20/month or $192/year
• Business: $40/user/month (includes team features and priority support)

v0 by Vercel

• Free: Limited generations per month
• Pro: $20/month or $192/year
• Enterprise: Custom pricing for teams

Security and Privacy Considerations

Data Protection

• GitHub Copilot: Enterprise-grade security, data not retained for training by default
• Cursor: Offers local model options, configurable data sharing policies
• v0: Vercel's standard security practices, code stored temporarily

Enterprise Features

• Audit logs - Track AI usage across your organization
• Policy management - Control which models and features are available
• Data residency - Some providers offer regional data processing
• Code review - Built-in tools to review AI-generated code before deployment

Best Practices for Secure AI Development

• Review all AI-generated code before committing to repositories
• Avoid sharing sensitive data in prompts and chat sessions
• Use enterprise versions for commercial projects requiring compliance
• Configure data retention policies according to your organization's requirements
• Train your team on AI security awareness and responsible usage

5) Customize GitHub Copilot in VS Code

There are extensive customization options available. You can set guidelines and rules for your project, and AI will maintain this context in each chat session.

Key Customization Features:

1. Instruction Files

• .github/copilot-instructions.md - Single file with project-wide coding standards, automatically applied to all chat requests
• .instructions.md files - Multiple specialized instruction files for specific tasks (e.g., TypeScript rules, React guidelines)
• Support for workspace-specific and user-wide instruction files

2. Custom Instructions in Settings Configure specific instructions for different scenarios:

• Code generation: github.copilot.chat.codeGeneration.instructions
• Test generation: github.copilot.chat.testGeneration.instructions
• Code review: github.copilot.chat.reviewSelection.instructions
• Commit messages: github.copilot.chat.commitMessageGeneration.instructions

3. Prompt Files (Experimental)

• Create reusable .prompt.md files for common tasks
• Support for variables and file references
• Can be shared across teams and projects
• Examples: React form generation, security reviews, onboarding guides

4. Best Practices

• Keep instructions short and specific
• Use multiple files to organize by topic
• Version control instruction files for team collaboration
• Use applyTo property to target specific file types
• Reference external files and documentation within prompts

Setup Steps:

1. Enable in settings: chat.promptFiles and github.copilot.chat.codeGeneration.useInstructionFiles
2. Create .github/copilot-instructions.md for project-wide rules
3. Add specific .instructions.md files in .github/instructions/ folder
4. Configure custom instructions in VS Code settings

For more details: https://code.visualstudio.com/docs/copilot/copilot-customization

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Conclusion: Your AI-Enhanced Development Journey

Key Takeaways

1. AI as a Development Partner

• AI tools augment rather than replace developers
• The most effective approach combines human expertise with AI assistance
• Focus on tools that integrate with your existing workflow

2. Strategic Tool Selection

• Choose models based on specific use cases (cost vs. performance)
• Consider your team's needs: enterprise features, security, pricing
• Start with free tiers to evaluate fit before committing to paid plans

3. Practical Implementation

• Begin with one tool (GitHub Copilot is often the safest starting point)
• Customize AI assistants with clear instructions and context
• Maintain good coding practices - they become even more important with AI

4. Security and Compliance

• Review your organization's data policies before adoption
• Use enterprise versions for sensitive or commercial projects
• Implement proper code review processes for AI-generated code

Next Steps

Immediate Actions:

1. Try a free tier - Start with GitHub Copilot, Cursor, or your preferred platform
2. Set up basic customization - Create your first .github/copilot-instructions.md file
3. Establish team guidelines - Define when and how to use AI tools in your projects

Medium-term Goals:

1. Evaluate ROI - Track productivity gains and cost-effectiveness
2. Expand usage - Gradually introduce AI tools across different development phases
3. Share knowledge - Build internal best practices and training resources

Long-term Strategy:

1. Stay informed - AI tools evolve rapidly; regularly reassess your toolkit
2. Scale adoption - Move from individual use to team-wide implementation
3. Contribute back - Share learnings with the development community

Resources for Continued Learning

• GitHub Copilot Documentation: https://docs.github.com/en/copilot
• VS Code Copilot Customization: https://code.visualstudio.com/docs/copilot/copilot-customization
• Cursor Documentation: https://docs.cursor.com/
• v0 by Vercel: https://v0.dev/docs

Questions and Discussion

This section is for live Q&A during the seminar presentation.

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Thank you for attending "AI Tools – What to Use & When". 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.