This comprehensive Generative AI Training, Evaluation, and Trends course equips you with the skills to build, optimize, and future-proof GenAI systems. Begin by learning how generative models are trained and evaluated using real-world metrics. Explore Retrieval Augmented Generation (RAG) to improve model accuracy by combining external data with LLMs. Progress into key trends shaping GenAI鈥攍ike scalable architectures, real-time applications, and model transparency鈥攚hile examining how these advancements apply across industries like healthcare, finance, and education.
Foundations of Generative AI Models
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Beginner level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Train and evaluate generative AI models using real-world techniques
Apply Retrieval Augmented Generation (RAG) to improve output accuracy
Understand emerging trends in GenAI architecture and deployment
Translate GenAI advancements into practical, industry-ready solutions
Skills you'll gain
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Recently updated!
June 2025
Assessments
6 assignments
Taught in English
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There are 2 modules in this course
Build a strong foundation in Generative AI with this module covering its importance, real-world impact, and core concepts. Understand why GenAI matters through relatable analogies and explore key model types, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based models. Ideal for beginners starting their GenAI journey.
What's included
8 videos1 reading3 assignments
Explore how Generative AI models are trained, evaluated, and enhanced using Retrieval Augmented Generation (RAG). Learn the key steps in model training, techniques to assess model quality, and understand how RAG improves output accuracy by combining retrieval and generation. Discover emerging trends shaping the future of GenAI and gain insights into evolving industry applications.
What's included
6 videos3 assignments
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Vanderbilt University
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Frequently asked questions
Generative AI models are algorithms that create new content such as text, images, or code; based on patterns learned from data. Common types include GANs, VAEs, and transformer-based models like GPT.
Foundation models are large-scale AI models trained on vast, diverse datasets and adaptable across a wide range of tasks. Examples include GPT, BERT, and CLIP.
The four models of AI are reactive machines, limited memory, theory of mind, and self-aware AI; representing increasing levels of complexity and cognitive capabilities.
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