Course structure
Day 1:
Introduction and Overview of Generative AI.
- Introductory Welcome and Agenda Overview.
- Brief introduction to generative AI, including ChatGPT and Assistant APIs.
- Key differences between generative AI and traditional AI solutions.
- Understanding the overall use of generative AI and its capabilities.
Generative AI Capabilities and Use Cases
- Exploring what generative AI can do exceptionally well.
- Detailed examples of the application of generative AI in solution architecture.
- Case studies of successful implementations.
Documentation of Requirements for Generative AI Solutions
- How requirements for generative AI solutions differ from traditional applications.
- Key considerations and challenges when documenting requirements.
- Techniques for capturing and defining requirements specific to generative AI.
- Practical examples and group discussions on best practices.
Interactive Application Analysis.
- Group activity to analyze given use cases and identify unique requirements.
- Discussion and feedback on the analysis.
- Q&A, summary of key learnings from day one.
Day 2:
Architectural Considerations for Generative AI.
- Overview of key architectural components specific to generative AI.
- Discussion of data sources, processing, model integration and APIs.
- Introduction to design patterns and best practices for generative AI applications.
Complexity Analysis and Performance Management
- Techniques for assessing and managing the complexity of generative AI projects.
- Practical examples and group discussions.
- Addressing the scalability and performance challenges of generative AI solutions.
- Best practices for performance optimization.
Practical Architecture Design
- Hands-on sessions for designing the architecture of a sample generative AI project.
- Group work and peer review of architectural designs.
- Techniques for effective documentation of architecture design.
- Practical examples and group activities.
Review and Future Trends.
- Presentation and review of architectural designs created during hands-on sessions.
- Feedback and discussion of improvements and best practices.
- Exploration of emerging trends and future developments in generative AI.
- Discussion on how to stay current and adapt to new advances.
- Q&A, final thoughts and feedback.
- Summary of key learnings and next steps.
