Generative AI Essentials on AWS – Intensive Training («AWSEAI»)

1 Introducing Generative AI

• Generative AI explained
• Foundation models
• AWS generative AI services
• Demo: Generative AI solution

2 Exploring Generative AI Use Cases

• Identify suitable use cases
• Generative AI applications and use cases
• Explore generative AI use case scenarios
• Use case for class

3 Essentials of Prompt Engineering

• Introduction to prompt engineering
• Prompt design best practices
• Advanced prompting strategies
• Model settings and parameters
• Hands-on Lab: Optimizing Slogan Generation with Amazon Bedrock

4 Responsible AI Principles and Considerations

• Introduction to responsible AI
• Core dimensions of responsible AI
• Generative AI considerations
• Hands-on Lab: Implementing Responsible AI Principles with Amazon Bedrock Guardrails

5 Security, Governance, and Compliance

• Security overview
• Adverse prompts
• Generative AI security services
• Governance
• Compliance

6 Implementing Generative AI Projects

• Introduction – Generative AI application
• Define a use case
• Select a foundational model
• Improve performance
• Evaluate results
• Deploy the application
• Demo: Amazon Q Business

7 Integrating Generative AI into the Development Lifecycle

• Introduction
• Hands-on Lab: Capstone – Creating a Project Plan with Generative AI

8 Course Wrap-up

• Next steps and additional resources
• Course summary

• Developers new to Generative AI – to build foundational understanding before coding with Bedrock
• Solution & Cloud Architects – to see where GenAI fits into AWS architectures and governance models
• Product & Project Managers – to scope and plan GenAI-enabled initiatives
• Business Analysts & Marketing/Sales Professionals – to identify concrete use cases and customer-facing opportunities
• IT & Line-of-Business Managers – to evaluate adoption, security, and cost implications