Master cutting-edge retrieval techniques that power today’s most advanced AI systems. In this course, you’ll explore sophisticated retriever patterns, vector databases, and indexing algorithms used in Retrieval-Augmented Generation (RAG). You’ll build a strong understanding of FAISS, ChromaDB, HNSW indexing, and the mechanics behind scalable similarity search in modern AI applications.
Through guided labs, you’ll implement advanced retrievers—including self-querying, multi-query, parent document retrievers, and vector-store-backed pipelines—using both LangChain and LlamaIndex. These exercises demonstrate how to significantly improve retrieval accuracy, reduce hallucinations, and deliver high-quality responses from large language models (LLMs).
You’ll bring everything together by building a complete RAG application that integrates FAISS for vector search, an advanced retriever for improved relevance, and a fully interactive Gradio interface. This end-to-end project strengthens your ability to design AI systems that understand context, surface precise information, and serve users in real time.
By the end of the course, you’ll have applied the latest retrieval strategies and database technologies to build production-ready RAG pipelines—skills that directly support careers in AI engineering, search systems, and applied machine learning.
