Generative AI Productivity - Architect's Wins and Pitfalls

Overview

Discover how Generative AI can boost software development productivity, with real-world insights on implementation wins and pitfalls from Wealthsimple's journey.

Syllabus

• Introduction to Generative AI
Overview of Generative AI technologies
Historical context and evolution
• Understanding Wealthsimple's Use Case
Background on Wealthsimple
Business goals for implementing Generative AI
• Key Concepts of Generative AI in Software Development
Types of Generative AI models
Processes enhanced by Generative AI
• Generative AI Tools and Platforms
Review of popular Generative AI tools
Selection criteria for AI platforms
• Implementation Strategies
Preparing your development environment
Integration with existing systems
Change management and team training
• Productivity Wins with Generative AI
Case studies of successful implementations
Metrics to measure productivity improvements
• Common Pitfalls and How to Avoid Them
Challenges in data quality and availability
Ethical concerns and bias in AI models
Managing expectations and monitoring AI performance
• Wealthsimple's Lessons Learned
Real-world examples of successes and failures
Reflections on iterative improvement
• Future Directions in Generative AI for Software Development
Emerging trends and technologies
Speculations on future applications
• Course Summary and Key Takeaways
Recap of main topics
Resources for further learning and exploration
• Final Q&A and Discussion
Open forum for questions
Sharing experiences and insights from participants
• Capstone Project (Optional)
Design a small-scale Generative AI project for a software development scenario
Group presentation and feedback session

Subjects

Computer Science