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 Duration 14 hours

Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI.
  • Examining the differences and synergies between the two.
  • Industry trends and diverse use cases.

Generative AI Architecture and Tools

  • Transformer models: GPT, LLaMA, Claude, and other key architectures.
  • Distinguishing between fine-tuning and in-context learning.
  • Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.

Prompt Engineering for Control and Structure

  • Effective prompt patterns for writing, coding, and summarization.
  • Techniques including few-shot, zero-shot, and chain-of-thought prompting.
  • Utilizing prompt libraries and testing frameworks.

Understanding Agentic AI

  • The definition and evolutionary path of agentic AI.
  • Core architectures: planning, memory, tool usage, and self-reflection.
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.

Designing and Deploying Autonomous Agents

  • Strategies for goal setting and task decomposition.
  • Integrating external tools and APIs (search, memory, code execution).
  • Multi-agent coordination and implementing human-in-the-loop supervision.

Use Cases and Implementation Scenarios

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

Summary and Next Steps

Requirements

  • A foundational understanding of AI and machine learning principles.
  • Professional experience with API development or scripting languages like Python.
  • Prior exposure to prompt engineering or the usage of Large Language Models.

Target Audience

  • AI developers and software engineers.
  • Innovation and R&D teams.
  • Technical product managers looking to explore agentic AI ecosystems.

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