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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.
  • Fine-tuning versus in-context learning.
  • Tools: ChatGPT, Hugging Face Transformers, Google AI Studio.

Prompt Engineering for Control and Structure

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

Understanding Agentic AI

  • Definition and evolution of agentic AI.
  • Architectures: planning, memory, tools, and self-reflection.
  • Popular frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.

Designing and Deploying Autonomous Agents

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

Use Cases and Implementation Scenarios

  • Content generation versus task orchestration.
  • Enterprise productivity, customer support, and data extraction.
  • Responsible and secure implementation practices.

Summary and Next Steps

Requirements

  • Fundamental understanding of AI and machine learning concepts.
  • Experience working with APIs or scripting languages such as Python.
  • Familiarity with prompt engineering or the usage of large language models.

Audience

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

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