How to Improve the Quality of Gen AI-Generated Code and Your Team's Dynamics

Overview

Discover strategies to enhance AI-generated code quality by providing better context and learn how team dynamics evolve when adopting generative AI coding assistants.

Syllabus

• Introduction to Generative AI in Software Development
Overview of generative AI technologies
Benefits and challenges of AI-generated code
• Understanding AI Contextual Needs
Importance of context in AI-generated outputs
Techniques for improving code quality by providing better context
• Strategies to Enhance AI-Generated Code Quality
Code structuring and style guidelines
Best practices for AI model fine-tuning
Continuous integration and testing with AI-generated code
• Adapting Team Dynamics to AI Integration
Impact of AI tools on team roles and responsibilities
Fostering collaboration between developers and AI
Managing resistance and promoting AI adoption
• Communication and Feedback Loops
Effective communication with AI systems
Establishing feedback mechanisms for continuous improvement
• Case Studies and Real-World Applications
Analysis of successful implementations of AI in coding
Lessons learned from failed attempts
• Ethics and Best Practices
Addressing ethical concerns in AI-generated code
Responsible AI usage and oversight
• Hands-On Workshop
Practical exercise: Code generation with AI tools
Group activity: Simulating team dynamics with AI assistance
• Evaluation and Future Directions
Assessing AI impact on code quality and team dynamics
Future trends in AI-driven software development
• Conclusion and Next Steps
Recap of learned strategies and tools
Guidance on continuing AI education and experimentation in coding

Subjects

Computer Science