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Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI
  • Understanding their differences and complementary roles
  • Industry use cases and emerging trends

Generative AI Architecture and Tools

  • Transformer models: GPT, LLaMA, Claude, and others
  • Fine-tuning versus in-context learning
  • Tools: ChatGPT, Hugging Face Transformers, Google AI Studio

Prompt Engineering for Control and Structure

  • Prompt patterns for writing, coding, summarization, etc.
  • Few-shot, zero-shot, and chain-of-thought prompting
  • Utilizing prompt libraries and testing tools

Understanding Agentic AI

  • Definition and evolution of agentic AI
  • Architectures: planning, memory, tools, self-reflection
  • Popular frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph

Designing and Deploying Autonomous Agents

  • Goal setting and task decomposition
  • Integrating tools and APIs (search, memory, code)
  • Multi-agent coordination and human-in-the-loop supervision

Use Cases and Implementation Scenarios

  • Content generation versus task orchestration
  • Enterprise productivity, customer support, data extraction
  • Responsible and secure implementation

Summary and Next Steps

Requirements

  • A foundational understanding of AI and machine learning concepts
  • Practical experience with APIs or scripting languages such as Python
  • Familiarity with prompt engineering or the usage of large language models

Audience

  • AI developers and engineers
  • Innovation and R&D teams
  • Technical product managers exploring agentic AI systems
 14 Hours

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