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

Overview

Delve into proven strategies to boost the quality of AI-generated code by supplying better context, all while gaining insights into the evolving team dynamics as generative AI coding assistants are embraced.

This educational content is perfect for those involved in Artificial Intelligence and Computer Science fields who wish to explore the integration of AI technologies into their programming workflow. Perfect for both beginners and seasoned professionals, this session promises to enrich your understanding of AI and its impact on teamwork and coding efficiency.

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

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Subjects

Computer Science