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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 current 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 techniques.
  •  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, and data extraction.
  •  Responsible and secure implementation practices.

Summary and Next Steps

Requirements

  •  A foundational understanding of AI and machine learning concepts.
  •  Experience working 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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