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 Duration 14 hours

Course Outline

Foundations of Generative and Agentic AI

  • Defining Generative AI and Agentic AI
  • Analyzing the differences and synergies between the two paradigms
  • Industry-specific use cases and emerging trends

Architecture and Ecosystem of Generative AI

  • Exploring transformer models, including GPT, LLaMA, and Claude
  • Contrasting fine-tuning with in-context learning methods
  • Key platforms: ChatGPT, Hugging Face Transformers, and Google AI Studio

Mastering Prompt Engineering for Precision

  • Developing prompt patterns for writing, coding, and summarization
  • Techniques involving few-shot, zero-shot, and chain-of-thought reasoning
  • Leveraging prompt libraries and validation tools

Deep Dive into Agentic AI

  • The evolution and definition of agentic frameworks
  • Core components: planning mechanisms, memory structures, tool usage, and self-reflection
  • Leading frameworks such as AutoGPT, BabyAGI, CrewAI, and LangGraph

Building and Launching Autonomous Agents

  • Strategies for goal definition and task breakdown
  • Connecting tools and APIs for search, memory management, and code execution
  • Coordinating multi-agent systems and implementing human oversight

Real-World Applications and Implementation

  • Distinguishing between content generation and complex task orchestration
  • Applications in enterprise productivity, customer service, and data processing
  • Ensuring secure and responsible deployment practices

Conclusions and Future Directions

Requirements

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

Target Audience

  • AI developers and software engineers
  • Innovation and R&D departments
  • Technical product managers seeking to implement agentic AI systems

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