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Course Outline

Introduction to Generative AI and Agentic AI

  • Defining the core concepts of Generative AI and Agentic AI.
  • Analyzing how these two approaches differ and synergize.
  • Reviewing key use cases and emerging trends across various sectors.

Generative AI Architecture and Tooling

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

Prompt Engineering for Precision and Structure

  • Developing prompt patterns for writing, coding, summarization, and more.
  • Mastering few-shot, zero-shot, and chain-of-thought prompting techniques.
  • Utilizing prompt libraries and validation tools for optimal results.

Deep Dive into Agentic AI

  • Defining the scope and evolutionary trajectory of agentic AI.
  • Core architectures: planning, memory management, tool usage, and self-reflection.
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.

Designing and Implementing Autonomous Agents

  • Strategies for goal definition and task decomposition.
  • Seamless integration of tools and APIs, including search, memory, and code execution.
  • Coordinating multi-agent systems with human-in-the-loop oversight.

Real-World Use Cases and Implementation

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

Conclusion and Future Directions

Requirements

  • A foundational grasp of AI and machine learning principles
  • Practical experience with APIs or scripting languages like Python
  • Basic familiarity with prompt engineering or the utilization of large language models

Target Audience

  • AI developers and software engineers
  • Innovation and Research & Development teams
  • Technical product managers investigating agentic AI ecosystems
 14 Hours

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