In this course, you will explore two techniques to improve the performance of a foundation model (FM): Retrieval Augmented Generation (RAG) and fine-tuning. You will learn about Amazon Web Services (AWS) services that help store embeddings with vector databases, the role of agents in multi-step tasks, define methods for fine-tuning an FM, how to prepare data for fine-tuning, and more.
Optimizing Foundation Models
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
1 hour to complete
Flexible schedule
Learn at your own pace
What you'll learn
Identify AWS services that help store embeddings with vector databases.
Understand the role of agents in multi-step tasks.
Understand approaches to evaluate FM performance and determine whether an FM effectively meets business objectives.
Skills you'll gain
Details to know
Recently updated!
October 2025
Assessments
1 assignment
Taught in English
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There is 1 module in this course
In this course, you will explore two techniques to improve the performance of a foundation model (FM): Retrieval Augmented Generation (RAG) and fine-tuning. You will learn about Amazon Web Services (AWS) services that help store embeddings with vector databases, the role of agents in multi-step tasks, define methods for fine-tuning an FM, how to prepare data for fine-tuning, and more.
What's included
1 reading1 assignment
Instructor
Offered by
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