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Become an AI Expert: Generative AI Basics

Posted on December 26, 2024  (Last modified on May 15, 2025) • 2 min read • 394 words
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Be Your Org's AI Leader

On this page
  • Introduction
  • Explore the Capabilities of Generative AI
    • Learning Objectives
    • Highlights
  • Understand the Technology Ecosystem of Generative AI
    • Learning Objectives
    • Highlights
  • Ready to Lead?
  • What’s Next?
Become an AI Expert: Generative AI Basics

Introduction  

In this series, we’ll take you from AI novice to AI expert! Kicking things off, we’re diving into the foundational concepts of generative AI. This post unpacks some core concepts and two matching Trailhead modules: Explore the Capabilities of Generative AI and Understand the Technology Ecosystem of Generative AI. Both provide insights to help you lead your organization confidently into the AI era.

Take the journey with us, and don’t forget to explore and pass the modules yourself on Trailhead!


Explore the Capabilities of Generative AI  

Learning Objectives  

  • Describe generative AI and its practical applications.
  • Identify generative AI’s benefits and limitations.

Highlights  

Generative AI excels in creative, repetitive, and complex data-driven tasks. It powers email drafting, predictive text, personalized marketing, image generation, and even music composition. The adaptability of this technology offers incredible opportunities but comes with challenges like hallucinations and ethical concerns.

To build trust with AI, businesses must prioritize accuracy, transparency, and respect for data security. Generative AI models shine when trained on reliable data and paired with defined business needs.


Understand the Technology Ecosystem of Generative AI  

Learning Objectives  

  • Identify key factors driving rapid generative AI development.
  • Describe the generative AI tech stack.
  • Address common business concerns with generative AI.

Highlights  

Generative AI’s rapid evolution is driven by:

  • Massive datasets providing diverse training materials.
  • The transformer architecture that supports complex language relationships.
  • Advances in parallel computing, enabling faster training.

The generative AI tech stack includes:

  • Compute hardware providers like Nvidia.
  • Cloud platforms from Google, AWS, and Microsoft.
  • Foundational models such as GPT-4 and LLaMA.
  • Optimization services for fine-tuning AI to specific business needs.
  • AI-powered applications and tools for end-users.

Despite its power, businesses must navigate concerns like data security, sustainability, and ethical usage. Proper AI strategy ensures value without compromising trust.


Ready to Lead?  

Get hands-on by completing the Explore the Capabilities of Generative AI module here and the Understand the Technology Ecosystem of Generative AI module here.

Take these modules to deepen your expertise, pass the quizzes, and establish your foundation as an AI leader!


What’s Next?  

In the next post in this series, we’ll explore Agentforce Agents and their role in revolutionizing organizational efficiency with autonomous AI. Stay tuned for actionable insights and hands-on advice!


Until then, keep working hard, smart, and happy. We’ll see you in the cloud!

 Become an AI Expert: Meet Agentforce
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On this page:
  • Introduction
  • Explore the Capabilities of Generative AI
    • Learning Objectives
    • Highlights
  • Understand the Technology Ecosystem of Generative AI
    • Learning Objectives
    • Highlights
  • Ready to Lead?
  • What’s Next?
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