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Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases

Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases

This course is part of AI Engineering Specialization

Instructor: Guil Hernandez

6,673 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

64 reviews

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

64 reviews

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Understand and Create Embedding
Utilize Vector Databases
Retrieval-Augmented Generation (RAG)

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91%

of learners achieved a positive career outcome

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

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Build your subject-matter expertise

This course is part of the AI Engineering Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

In this course, you will explore advanced AI engineering concepts, focusing on the creation, use, and management of embeddings in vector databases, as well as their role in Retrieval-Augmented Generation (RAG).

You will start by learning what embeddings are and how they help AI interpret and retrieve information. Through hands-on exercises, you will set up environment variables, create embeddings, and integrate them into vector databases using tools like Supabase. As you progress, you will take on challenges that involve pairing text with embeddings, managing semantic searches, and using similarity searches to query data. You will also apply RAG techniques to enhance AI models, dynamically retrieving relevant information to improve chatbot responses. By implementing these strategies, you will develop more accurate, context-aware conversational AI systems. This course balances both the theory behind AI embeddings and RAG with practical, real-world applications. By the end, you will have built a proof of concept for an AI chatbot using RAG, preparing you for more advanced AI engineering tasks.