Advanced RAG with Vector Databases and Retrievers

Overview

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.

Syllabus

Advanced Retrievers for RAG

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.

Build a Comprehensive RAG Application

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.