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Advanced RAG with Vector Databases and Retrievers

Advanced RAG with Vector Databases and Retrievers

This course is part of multiple programs.

Instructors: Wojciech 'Victor' Fulmyk

10,039 already enrolled

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2 modules

Gain insight into a topic and learn the fundamentals.

55 reviews

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

55 reviews

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Build RAG applications using vector databases and advanced retrieval patterns
Employ the core mechanics of Vector Databases such as FAISS and Chroma DB and implement indexing algorithms like HNSW
Implement advanced retrievers using LlamaIndex and LangChain to improve the quality of LLM responses
Develop comprehensive RAG applications by integrating LangChain, FAISS, and front-end user interfaces built using Gradio

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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There are 2 modules in this course

Ready to boost your AI career by mastering next-level retrieval techniques for intelligent search and summarization? This hands-on course takes you deep into the world of Retrieval-Augmented Generation (RAG), advanced retrievers, and vector databases such as FAISS and Chroma DB. You'll gain the cutting-edge skills businesses need to design and build scalable, high-performance RAG applications that drive smarter search and response capabilities.

During the course, you'll learn how to differentiate retrieval patterns, implement similarity search using FAISS, and integrate LangChain with modern UI frameworks such as Gradio. Then, in practical labs and guided projects, you'll get hands-on experience building an end-to-end AI application that retrieves, summarizes, and answers questions in real time. From multi-query and parent document retrievers to semantic vector search and evaluation, this course will give you the skills to improve internal search engines, chatbot accuracy, and content recommendation systems. Enroll today and enhance your portfolio with hands-on experience building AI that understands context—and delivers results.

In this module, you will get a deep dive into advanced retrievers and retrieval patterns, equipping you with the skills to implement and optimize advanced retrieval strategies within a RAG system. Participants will explore various retriever types through video lectures and hands-on labs, including vector store-backed, multi-query, self-querying, and parent document retrievers. Learners will apply these techniques using LangChain and LlamaIndex, gaining practical experience in building smarter search capabilities and enhancing retrieval efficiency in AI-driven applications.

What's included

5 videos2 readings3 assignments4 app items1 discussion prompt2 plugins

5 videos Total 26 minutes

Course Introduction 4 minutes
RAG and Agentic AI Professional Certificate Overview 6 minutes
Explore Advanced Retrievers in Langchain: Part 1 3 minutes
Explore Advanced Retrievers in Langchain - Part 2 5 minutes
Advanced Retrievers in LlamaIndex 9 minutes

2 readings Total 8 minutes

Course Overview 5 minutes
Summary and Highlights: Advanced Retrievers for RAG 3 minutes

3 assignments Total 49 minutes

Graded Quiz: Advanced Retrievers for RAG 21 minutes
Practice Quiz: Work with Advanced Retrievers in LangChain 14 minutes
Practice Quiz: Work with Advanced Retrievers in LlamaIndex 14 minutes

4 app items Total 140 minutes

Lab: Build a Smarter Search with LangChain Context Retrieval 60 minutes
[Optional] Interactive LangChain Lesson Recap Podcast (AI-Powered) 10 minutes
Lab: Explore Advanced Retrievers in LlamaIndex 60 minutes
[Optional] Interactive LlamaIndex Lesson Recap Podcast (AI-Powered) 10 minutes

1 discussion prompt Total 2 minutes

[Optional] Meet and Greet 2 minutes

2 plugins Total 20 minutes

Reading: Helpful Tips for Course Completion 5 minutes
Reading: Cheat Sheet: Advanced Retrievers for RAG 15 minutes

In this module, you will explore FAISS, a powerful vector database used for efficient similarity search. You will compare FAISS with Chroma DB to understand its unique advantages and applications. Through hands-on experience, you will build a semantic search engine using FAISS in a non-RAG setting, demonstrating its versatility beyond retrieval-augmented generation (RAG). Finally, you will develop a fully functional RAG application, integrating FAISS, an advanced retriever, and a front-end UI built with Gradio. This module reinforces key RAG concepts while guiding learners through the process of creating an end-to-end AI-powered application.

What's included

2 videos3 readings2 assignments3 app items2 plugins

2 videos Total 12 minutes

Introduction to FAISS and How It compares to ChromaDB 5 minutes
Course Wrap-Up 7 minutes

3 readings Total 6 minutes

Summary and Highlights: Build a Comprehensive RAG Application 2 minutes
Congratulations and Next Steps 2 minutes
Team and Acknowledgements 2 minutes

2 assignments Total 31 minutes

Graded Quiz: Build a Comprehensive RAG Application 21 minutes
Practice Quiz: Introduction to FAISS for RAG 10 minutes

3 app items Total 130 minutes

Lab: Semantic Similarity with FAISS 60 minutes
Lab: AI-Powered YouTube Summarizer, QA Tool with RAG, LangChain, FAISS 60 minutes
[Optional] Interactive FAISS/HNSW Lesson Recap Podcast (AI-Powered) 10 minutes

2 plugins Total 45 minutes

Reading: Hierarchical Navigable Small World (HNSW) 30 minutes
Cheat Sheet: Build a Comprehensive RAG Application 15 minutes

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Instructors

Instructor ratings

(8 ratings)

Wojciech 'Victor' Fulmyk

IBM

9 Courses 116,122 learners

Offered by

IBM

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Reviewed on Mar 13, 2026

Its a wonderful course I got so far. I completed this and got good grasp in advanced vector DBs and finally ended with good profile of Gen AI Engineer lead role. Must watch.

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Frequently asked questions

By mastering advanced RAG techniques and vector databases such as FAISS and Chroma DB, and learning to integrate with LangChain and Gradio, you'll be well-prepared for roles such as AI Developer, Data Engineer, AI Application Architect, Search Algorithm Engineer, or Technical Product Manager. These roles involve developing intelligent, efficient search systems, optimizing retrieval methods, and designing AI-driven applications that utilize advanced retrieval techniques.

No, machine learning experience is not a requirement! Although Python programming and an understanding of APIs and web development are recommended, this course focuses on implementing and optimizing retrieval systems using tools such as FAISS, LangChain, and Gradio. It's designed for developers and engineers looking to enhance their skills in building advanced search-driven AI applications without delving deeply into machine learning model training.

Traditional courses often focus on basic query optimization or relational databases. In contrast, this course dives deep into Retrieval-Augmented Generation (RAG) and advanced vector-based retrieval systems. You'll explore cutting-edge techniques such as similarity search, vector databases, and AI-driven retrieval strategies, applying these concepts to create dynamic, real-time, and context-aware search experiences. It's perfect for developers looking to leverage modern technologies for AI-enhanced search systems.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.