Training on Applied Generative AI for Business and Digital Transformation

Module 1: History, Development, and Current Applications of Generative AI

• Tracing the evolution of AI from rule-based systems to generative models
• Understanding the key milestones in generative AI development
• Exploring the current state of generative AI and its capabilities
• Identifying the limitations and challenges of generative AI
• Analyzing the business impact of generative AI across sectors
• Case Study: Examining the evolution of a generative AI technology

Module 2: Diverse Applications of Generative AI Across Industries

• Exploring generative AI applications in marketing, content creation, and design
• Understanding generative AI in software development and code generation
• Examining generative AI in healthcare, finance, and legal sectors
• Identifying industry-specific use cases and opportunities
• Evaluating the potential of generative AI for innovation
• Case Study: Analyzing a successful generative AI implementation in an industry

Module 3: Understanding and Implementing Prompt Engineering for Productivity

• Understanding the principles of effective prompt engineering
• Learning different prompting techniques: zero-shot, few-shot, chain-of-thought
• Practicing prompt design for various business tasks
• Refining prompts through iteration and testing
• Developing prompt libraries for repeatable tasks
• Case Study: Building a prompt engineering workflow for a business function

Module 4: Strategies for Automating Organizational Workflows with Generative AI

• Identifying automation opportunities in business processes
• Designing AI-powered workflows for efficiency
• Implementing automation with generative AI tools
• Managing change and adoption in automated workflows
• Measuring the impact of automation on productivity
• Case Study: Automating a business workflow with generative AI

Module 5: Understanding the Dynamics of Reinforcement Learning and Data Search in Generative AI

• Understanding the role of reinforcement learning in generative AI
• Exploring data search and retrieval-augmented generation (RAG)
• Implementing knowledge base integration for enhanced AI responses
• Managing data quality and relevance in generative AI systems
• Optimizing search and retrieval for better AI outputs
• Case Study: Implementing RAG for a domain-specific application

Module 6: Managing Ethics, Compliance, and Risks in Generative AI

• Identifying ethical challenges in generative AI
• Managing bias, fairness, and accountability in AI outputs
• Ensuring compliance with data protection and privacy regulations
• Developing risk mitigation strategies for generative AI
• Building responsible AI governance frameworks
• Case Study: Conducting a risk assessment for a generative AI project

Module 7: Unlocking New Digital Transformation Opportunities with Generative AI

• Identifying strategic opportunities for generative AI
• Developing a generative AI vision and roadmap
• Aligning generative AI initiatives with business strategy
• Building a business case for generative AI investments
• Leading generative AI-driven digital transformation
• Case Study: Developing a generative AI transformation strategy

Module 8: Identifying Your Organization's Technological and Cultural Needs for AI

• Assessing organizational readiness for generative AI
• Identifying technology infrastructure requirements
• Evaluating data availability and quality for AI
• Assessing workforce skills and training needs
• Developing a change management plan for AI adoption
• Case Study: Conducting an organizational AI readiness assessment

Module 9: Building with AI Tools: ChatGPT, LangChain, AutoGPT, and Vector Search

• Getting started with ChatGPT and OpenAI APIs
• Building applications with LangChain for LLM orchestration
• Implementing AutoGPT for autonomous task completion
• Using vector search for knowledge retrieval
• Integrating multiple AI tools for complex workflows
• Case Study: Building a multi-tool generative AI application

Module 10: Case Studies on Successful GenAI Implementation for Digital Transformation

• Analyzing successful generative AI implementations
• Identifying lessons learned and best practices
• Developing a generative AI implementation roadmap
• Managing stakeholder expectations and communication
• Evaluating the impact of generative AI on business outcomes
• Case Study: Developing a generative AI implementation plan