Description

Boost your team’s speed, accuracy, and analytic insights by equipping them with essential Generative AI (GenAI) skills. This Generative AI course provides a practical introduction to GenAI, covering key concepts and prompt engineering techniques to automate tasks, improve content creation, and analyze data more effectively. Participants learn how to leverage GenAI for various use cases, from generating marketing copy to summarizing complex documents. This course also includes AI ethics and risk management. Hands-on labs simulate real-world scenarios, giving participants the confidence to immediately apply their new GenAI skills to their daily work. No coding experience is required!

By the end of this course, participants will be able to:

Enhance productivity and innovation with key GenAI concepts
Understand how Large Language Models (LLMs) work
Automate and optimize processes with prompt engineering
Make informed decisions by analyzing GenAI benefits and challenges
Improve content creation and data analysis with effective prompts
Understand the NIST AI Risk Management Framework

Audience

Anyone interested in learning basic GenAI principles

Course Overview

Understanding Generative AI

What is Intelligence?
Mechanisms of Intelligence
What is Artificial Intelligence?
How does AI work?
What can AI do?
What is Generative AI?
How does a Large Language Model work?
Tokenization
Example Tokenization: GPT-4o
Embeddings
Predicting the Next Token
LLM Settings
How are LLMs trained?
How do multi-modal models work?
Generative AI architecture
Benefits and Challenges of Generative AI
Common Frameworks and Models
Common Terms in Generative AI
Reflection

Basics of Prompt Engineering

Basics of Prompting
What is Prompt Engineering?
Why It’s Both Art and Science
Communicating with Large Language Models
Prompt Engineering and LLM Limitations
Importance of Effective Prompting
Instructions and Questions
Personas and Conditional Prompts
Key Principles of Effective Prompts
Principles of Effective Prompt Design
Anatomy of an Effective Prompt
Methodological Instructions
Personas and Constraints
Use Examples for Effective Outputs
Asking for Reflection (Inner Monologue)
Layering Prompts
Adapting to Intended Audience
Emphasizing Specific Details
Putting it Together: Translation and Data Analysis
Example: Technical Support Response
Common NLP Tasks
Summarization and Classification
Named Entity Recognition (NER)
Summary: Effective Prompting Strategies
Case Study: Customer Support Chatbot
Balancing Detail with Conciseness
Key Takeaways: Prompt Engineering
Resources for Mastering Prompt Engineering

Real-world Use Cases and Ethics of AI/ML

Where will AI be used?
Personalized Learning and Fraud Detection
Medical Diagnosis and Customer Service
Predictive Maintenance and Autonomous Vehicles
Crop Monitoring, Climate Modeling, and More
Ethics Introduction and Progression
The Point of AI Ethics
NIST AI Risk Management Framework
NIST AI RMF Characteristics of Trust
NIST Generative AI Risks
Blueprint for an AI Bill of Rights
European Union AI Risk Categories
Reflection on AI Ethics

Introduction to Generative AI Concepts Virtual Classroom Live December 18, 2025

Price: $825

Enroll today to reserve your spot!

Description

Course Overview

Understanding Generative AI

Basics of Prompt Engineering

Real-world Use Cases and Ethics of AI/ML

Prerequisites

Other Available Dates for this Course

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