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Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI.
  • Key differences and complementary strengths.
  • Industry use cases and current trends.

Generative AI Architecture and Tools

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

Prompt Engineering for Control and Structure

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

Understanding Agentic AI

  • Definition and historical evolution of agentic AI.
  • Core architectures: planning, memory, tool use, and self-reflection.
  • Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.

Designing and Deploying Autonomous Agents

  • Goal setting and task decomposition strategies.
  • Integration of tools and APIs (search, memory, code execution).
  • Multi-agent coordination and human-in-the-loop supervision.

Use Cases and Implementation Scenarios

  • Comparing 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 concepts.
  • Practical experience with APIs or scripting languages such as Python.
  • Familiarity with prompt engineering techniques or the usage of large language models.

Audience

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

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