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.
Advanced RAG with Vector Databases and Retrievers
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Advanced RAG with Vector Databases and Retrievers
This course is part of multiple programs.
Instructors: Wojciech 'Victor' Fulmyk +1 more
2,294 already enrolled
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4.9
(15 reviews)
Intermediate level
Recommended experience
7 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
- Category: Performance Tuning
- Category: Semantic Web
- Category: User Interface (UI)
- Category: Natural Language Processing
- Category: Application Development
- Category: Artificial Intelligence
- Category: Database Management Systems
- Category: Scalability
- Category: Query Languages
- Category: Generative AI
- Category: Large Language Modeling
- Category: Data Processing
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August 2025
Assessments
5 assignments
Taught in English
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There are 2 modules in this course
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
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
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Instructors
Wojciech 'Victor' Fulmyk
IBM
8 Courses76,576 learners
Offered by
IBM
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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.