Generative AI Tools for Technical Leaders (1 day)
Generative AI has transformed the development landscape. What is the best way to role out and deploy these tools to your technical teams. How do you decide which tools to use, and how do you ensure good governance and security? This course demonstrates both the current capability landscape, and also strategies you can adopt when rolling out these tools to your technical teams.
Prerequisites
- Some previous development and technical experience would be beneficial
Contents
Generative AI and the SDLC
- Overview of the current AI coding tool landscape
- GitHub Copilot, Cursor, Amazon Q, Claude, ChatGPT, and specialized tools
- AI coding assistants: inline suggestions vs chat vs agent mode
- Tools for code generation and multi-file editing
- AI-powered test generation and documentation
- Processing Pull Requests with AI assistance
- Automated security reviews and vulnerability detection
- AI for ticket analysis and estimation
- Using AI for Tier 1 support and issue triage
AI-Powered Code Generation and Agent Mode
- Understanding AI agent mode capabilities
- Multi-file editing and workspace context awareness
- Prompt engineering for developers: effective code generation
- Context management strategies for better AI assistance
- Multi-turn conversations with AI coding assistants
- When to use inline suggestions vs chat vs agent mode
- Code generation patterns and anti-patterns
- AI-assisted refactoring at scale
- Automated migration between frameworks or languages
Leadership Strategies
- Current thinking around effective AI tool rollouts
- Tool selection criteria: evaluation framework for your organization
- Cloud vs on-premise vs hybrid deployment decisions
- Data privacy, IP protection, and license compliance
- Creating AI usage policies and guardrails
- Changing culture and building AI literacy
- Collaborative approaches across teams
- Balancing innovation with governance
The Rollout
- Phased rollout strategies: pilot programs to full deployment
- Creating clear usage guidelines and best practices
- Developer training programs and enablement
- Building internal champions and communities of practice
- Showcasing successes and learning from failures
- Continuous feedback loops and iteration
Metrics and ROI
- Defining success criteria for AI tool adoption
- Code velocity metrics: commit frequency, PR cycle time
- Code quality measurements: bug rates, security vulnerabilities
- Developer satisfaction and productivity surveys
- ROI calculations and cost-benefit analysis
- Industry benchmarks and case studies
- Stories and lessons learned from other organizations
Hands-on Workshop with AI Coding Tools
- Hands-on with GitHub Copilot and agent mode
- Building features from natural language descriptions
- Generating comprehensive test suites with AI
- Refactoring legacy code with AI assistance
- Debugging applications using AI-powered analysis
- Automating documentation generation
- Code review with AI assistance
- Comparing different AI coding assistants
Pitfalls and Safeguards
- Understanding the limits and failure modes of AI code generation
- Hallucinations in code: recognizing and mitigating incorrect suggestions
- Security vulnerabilities in AI-generated code
- License compliance and intellectual property concerns
- Code review practices for AI-assisted development
- Over-reliance on AI and developer skill degradation
- Accountability and responsibility for AI-generated code
- Maintaining code quality standards with AI assistance
- The evolving landscape of AI in software development
