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 Duration 14 hours

Course Outline

Foundations of Generative and Agentic AI

  • Defining Generative AI and Agentic AI
  • Distinguishing between the two and understanding their complementary roles
  • Industry-specific use cases and emerging trends

Generative AI Architecture and Tooling

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

Prompt Engineering for Precision and Structure

  • Developing prompt patterns for writing, coding, and summarization
  • Techniques including few-shot, zero-shot, and chain-of-thought prompting
  • Leveraging prompt libraries and testing utilities

Insights into Agentic AI

  • Evolution and definition of agentic AI concepts
  • Core architectural elements: planning, memory, tool usage, and self-reflection
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph

Building and Deploying Autonomous Agents

  • Setting goals and breaking down complex tasks
  • Seamless integration of tools and APIs for search, memory, and code execution
  • Coordinating multi-agent systems and implementing human-in-the-loop oversight

Practical Use Cases and Implementation

  • Contrasting content generation with task orchestration
  • Applications in enterprise productivity, customer support, and data extraction
  • Ensuring secure and responsible implementation practices

Conclusion and Future Directions

Requirements

  • Foundational knowledge of AI and machine learning principles
  • Hands-on experience with APIs or scripting languages like Python
  • Working familiarity with prompt engineering or the utilization of large language models

Target Audience

  • AI developers and software engineers
  • Innovation and R&D teams
  • Technical product managers investigating agentic AI systems

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