Generative AI for Software Development

Principles of Generative AI in Software Development

• What is Generative AI?
• Generative AI Use Cases
• Generative AI Benefits
• Myths Around Generative AI
• Challenges of Generative AI
• Generative AI for Software Development
• How Developers Should Evolve with Generative AI
• Summary
• Expanding Your Generative AI Knowledge: SLMs, LLMs, and LMMs
• Foundation Models in Generative AI
• How to Start with Generative AI
• Code Generation Using Generative AI
• Agentic AI Workflows
• How Generative AI is Becoming Democratized
• Summary
• Why Prompt Engineering?
• Prompt Techniques
• Prompt Use Cases for the Software Development Lifecycle (SDLC)
• Prompt Management Cycle and Best Practices 
• Prompt Engineering Tools
• Summary

Enhancing Coding and Debugging with AI

Ethics of AI in Software Engineering

• Why the New Concerns?
• Bias in AI-Generated Code
• Model Architecture and Optimization Bias
• Human Feedback Bias
• Strategies to Mitigate Bias 
• Prompt Safety and Security for Responsible AI
• Intellectual Property (IP) Considerations
• Privacy Concerns in Generative AI
• Key AI Laws and Guidelines
• Security Risks in AI Applications
• Guardrails for Secure Use of Generative AI Applications
• Observability from an Ethical AI Perspective
• Summary

Evaluating AI’s Impact on Software Engineering

• Impact of Generative AI on Software Development
• Essential Tools and Frameworks for Gen AI-Based Software Application Development
• GenAI Ops: Operationalizing Generative AI Applications
• Summary
• Industry Study on Developer Productivity with Generative AI
• Transforming Software Development with Generative AI in the SDLC
• Generative AI for Specific Programming Tasks
• End-to-End AI Integration in the SDLC
• Challenges and Tradeoffs in AI Integration
• Key Metrics and KPIs for Measuring AI Impact 
• Next Steps: Sustaining and Expanding AI Integration
• The Future Outlook 
• Summary

Advanced Applications of GAI in Coding and Debugging

• What is Model Fine-Tuning?
• Model Evaluation
• LLM Benchmarking
• Building Well-Architected Gen AI Applications
• Well-Architected Framework Pillars for GenAI Applications
• Summary
• Building SkillGenie - Problem Statement 
• SkillGenie – Features
• SkillGenie User Journey 
• System Design for SkillGenie
• API Design 
• Prototype Development
• Safe use of AI and content moderation
• Enhancing SkillGenie outputs using Agentic AI 
• Production Launch
• Post-Production Monitoring 
• Summary