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Course Outline

Introduction to Generative AI and Agentic AI

  • Defining Generative AI and Agentic AI.
  • Distinguishing features and complementary aspects of each approach.
  • Industry-wide use cases and current trends.

Generative AI Architecture and Tools

  • Transformer-based models: GPT, LLaMA, Claude, and more.
  • Fine-tuning techniques versus in-context learning.
  • Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio.

Prompt Engineering for Control and Structure

  • Effective prompt patterns for writing, coding, summarization, and other tasks.
  • Techniques including few-shot, zero-shot, and chain-of-thought prompting.
  • Utilizing prompt libraries and testing environments.

Understanding Agentic AI

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

Designing and Deploying Autonomous Agents

  • Setting goals and decomposing complex tasks.
  • Integrating tools and APIs for search, memory, and code execution.
  • Coordinating multi-agent systems and implementing human-in-the-loop supervision.

Use Cases and Implementation Scenarios

  • Differentiating between content generation and task orchestration.
  • Applications in enterprise productivity, customer support, and data extraction.
  • Strategies for responsible and secure implementation.

Summary and Next Steps

Requirements

  • A foundational grasp of artificial intelligence and machine learning principles.
  • Practical experience using APIs or scripting languages, particularly Python.
  • Familiarity with prompt engineering techniques or the general usage of large language models.

Target Audience

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

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Provisional Upcoming Courses (Require 5+ participants)

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