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 Duration 14 hours

Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI
  • Analyzing their differences and complementary strengths
  • Key use cases and emerging trends across various industries

Generative AI Architecture and Tools

  • Transformer models: GPT, LLaMA, Claude, and alternatives
  • Distinguishing between fine-tuning and in-context learning
  • Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio

Prompt Engineering for Control and Structure

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

Understanding Agentic AI

  • Core definitions and the evolution of agentic AI
  • Architectural components: planning, memory, tool usage, and self-reflection
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph

Designing and Deploying Autonomous Agents

  • Establishing goals and decomposing tasks
  • Integrating external tools and APIs (search, memory, code execution)
  • Coordinating multi-agent systems 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

  • Foundational knowledge of AI and machine learning principles
  • Practical experience with APIs or scripting languages such as Python
  • Existing familiarity with prompt engineering or the utilization of large language models

Target Audience

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

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