Learn to use generative AI to improve business processes.
8 hour basics course
INTRODUCTION TO THE COURSE
The course provides a comprehensive overview of generative AI, code assistants, multimodal content generation, AI agents, and practical guidance on applying these tools to improve business processes.
This course provides a foundational preparation, covering fundamental concepts, hands-on activities, ethical considerations, and strategic integration, allowing students to practically apply generative AI, code assistants, image/video tools, and AI agents to improve efficiency, creativity, and innovation in their daily business operations.
COURSE PROGRAM
1. Origins and history of AI
- The origins and history of the evolution of AI.
- AI and society, opinions and fears.
2. AI Today, generative and agent-based artificial intelligence
- Definitions: AI, Machine Learning, Generative AI and AI Agents
- Overview of large language models (LLMs) and fundamental models (e.g., GPT, Gemini)
- Key use cases: text generation, coding assistance, image/video creation, autonomous decision making (AI agents)
3. Fundamental concepts and terminology
- Basics of data quality and model training
- Key performance metrics and evaluation methodologies for generative models and agents
4. Work with LLMs and code assistants
- Understand prompts, prompt engineering, and how LLMs generate output
- Techniques for creating effective prompts, refining results, and building knowledge bases
- Hands-on experience with ChatGPT, Google Gemini, or Microsoft Copilot
- Practical exercises: generating marketing content, writing reports, writing
- Integrate code assistants into your development workflow for faster prototyping, debugging, and documentation
5. Multimodal generation (images and videos)
- Overview of image generation tools (e.g., DALL·E, Midjourney)
- Video generation tools and synthetic media platforms
- Exercises: creating simple images for presentations or marketing materials; outline training video content
6. Introducing AI Agents and Process Automation
- What are AI agents? Autonomous execution of tasks with minimal human supervision
- Frameworks and tools (e.g. LangChain, Auto-GPT, other agent-based frameworks)
7. Ethics, compliance and governance considerations
- Address bias, misinformation, and intellectual property issues in generated content
- Privacy, data protection and regulatory compliance (GDPR, CCPA, etc.)
- Best practices for responsible AI deployment, model governance, and testability
8. Integration and implementation strategies
- Evaluation of AI platforms and APIs (OpenAI, Google Cloud AI, Azure OpenAI, etc.)
- Incorporate LLMs, code assistants, multimodal tools, and agents into existing workflows and applications
9. Future trends, application brainstorming and Q&A
- Emerging capabilities in multimodal LLMs, increased agent autonomy, and cross-domain integration
- Discussion: Identify internal processes ripe for automation and improvement
- Q&A session: Address participants’ specific challenges and explore customized solutions
