In this 2-hour project-based course, you will learn how to import data into Pandas, create embeddings with SentenceTransformers, and build a retrieval augmented generation (RAG) system with your data, Qdrant, and an LLM like Llamafile or OpenAI. This hands-on course will teach you to build an end-to-end RAG system with your own data using open source tools for a powerful generative AI application.
Introduction to Retrieval Augmented Generation (RAG)
Introduction to Retrieval Augmented Generation (RAG)
Instructor: Alfredo Deza
Access provided by Coursera Learning Team
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Learn, practice, and apply job-ready skills with expert guidance
4.4
(38 reviews)
Intermediate level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
Create a Retrieval Augmented Generation system
Use your own data with a Large Language Model
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
No downloads or installation required
Only available on desktop
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Create Embeddings
Recommended experience
Some experience with Python, including installing dependencies. Familiarity with the terminal is recommended
4 project images
Instructor
Offered by
How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Learner reviews
4.4
38 reviews
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63.15%
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23.68%
- 3 stars
2.63%
- 2 stars
7.89%
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2.63%
Showing 3 of 38
JC
5
Reviewed on May 17, 2024
Good Exercise for introduction RAG by LLM and VectorDB
ST
5
Reviewed on Oct 7, 2024
The guided project helped me understand RAG and its application in a concise and accurate manner. The project code was very helpful in understanding the workflow of implementing RAG with LLMS.
PP
5
Reviewed on Mar 20, 2025
It was a good entry point to the RAG's world, I hope to see more professional courses too.
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