Enqurious LogoTM

Use coupon code 'ENQURIOUS25' to get 10 credits for FREE

Ending in
0
0
Days
0
0
Hours
0
0
Minutes
0
0
Seconds
Go back

Introduction to Generative AI in Snowflake

2 Scenarios
2 Hours
Intermediate
item card poster cover image
Free
popular-iconPopular
5 credits
Industry
e-commerce
Skills
ai-modelling
generative-ai
Tools
snowflake

Learning Objectives

Understand the fundamental difference between traditional AI and Generative AI
Learn how Large Language Models process language using tokens and transformer architecture
Explore the training process behind modern LLMs and what makes them "large"
Understand real-world applications and use cases across different industries

Overview

In today’s rapidly evolving digital ecosystem, professionals face a new kind of challenge—understanding Generative AI beyond the buzzwords. Traditional AI once relied on explicit programming and structured outputs. Now, with Generative AI, machines can create—writing, coding, designing, and conversing like humans. For many, this transformation feels complex and inaccessible.

Ignoring how Generative AI works means more than just missing a trend—it risks losing competitive advantage. Organizations leveraging GenAI are already automating workflows, improving decision-making, and delivering hyper-personalized experiences. Those without a solid grasp struggle to differentiate hype from true capability, often misusing AI tools or failing to capture their full potential.

Through engaging conversations between Sam, a data analyst, and Alex, a senior AI architect, you’ll navigate how AI systems generate content, make decisions, and learn from data. Interactive scenarios, hands-on demonstrations, and knowledge checks will help you build both conceptual understanding and real-world intuition.

Prerequisites

  • General awareness of AI concepts (e.g., knowing that AI involves machine learning)
  • Data concepts such as datasets, training data, and features — no deep coding knowledge required.
  • Analytical thinking and curiosity to explore how models process information and generate responses.
  • Everyday AI tools (e.g., chatbots, virtual assistants, or AI writing tools) to relate theory to real-world use.