Duke University

Introduction to Generative AI

This course is part of Large Language Model Operations (LLMOps) Specialization

Taught in English

Noah Gift
Alfredo Deza
Derek Wales

Instructors: Noah Gift

1,978 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

37 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn to utilize Generative AI for automation.

  • Develop Generative AI software solutions.

  • Build solutions with Prompt Engineering to enhance Generative AI output.

Details to know

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Recently updated!

February 2024

Assessments

4 quizzes, 11 assignments

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

37 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Large Language Model Operations (LLMOps) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 4 modules in this course

This week, you will learn what generative AI is and how it has evolved from early AI to the large language models used today. You'll understand how these models work in applications by learning about model architectures and the training process. The week provides an overview of major foundation models like ChatGPT and Hugging Face, highlighting their capabilities and limitations. You'll explore the generative AI landscape, comparing options like open source models, local models, and cloud APIs. By the end, you'll have a solid base of knowledge about the foundations of this technology and options for accessing and leveraging different AI systems.

What's included

21 videos10 readings1 quiz3 assignments1 discussion prompt

This week, you will learn the fundamentals of prompt engineering to interact effectively with generative AI models. You'll understand the concept of few-shot prompting and practice basic prompting techniques using context and examples. Building on this, you'll learn methods for improving prompts through personas, detailed instructions, and iteration based on feedback. Finally, you'll explore more advanced skills like breaking down tasks, chaining prompts, and other useful techniques to overcome context limitations.

What's included

18 videos5 readings1 quiz3 assignments

This week, you will explore different types of generative AI applications, including API-based, embedded model, and multi-model systems. You'll learn the fundamentals of building robust applications using techniques like Retrieval Augmented Generation (RAG) to improve context. Through hands-on exercises, you'll gain experience testing an application locally and deploying it on the cloud.

What's included

19 videos5 readings1 quiz2 assignments1 ungraded lab

Here, you will learn the key capabilities of the OpenAI API. You will generate images with OpenAI’s DALL-E, “fine tuning” LLM models to Reddit questions and answers and summarize videos with OpenAI’s Whisper Model.

What's included

19 videos8 readings1 quiz3 assignments1 ungraded lab

Instructors

Noah Gift
Duke University
40 Courses90,738 learners

Offered by

Duke University

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