This advanced course focuses on building powerful RetrievalQA and Retrieval Augmented Generation (RAG) systems using LangChain and modern vector databases. It is designed for developers who want to move beyond basic RAG setups and build flexible, high-performance retrieval pipelines.

The course begins with RetrievalQA over multiple files using ChromaDB, including working with PDFs and structured document collections. Learners explore different embedding strategies such as Instructor embeddings, BGE embeddings, and lightweight micro-embeddings optimized for edge and mobile devices.

A strong emphasis is placed on running RAG pipelines with local and open-source large language models, including LLaMA 2 variants, eliminating reliance on proprietary APIs. Custom prompt engineering for RetrievalQA is covered to improve answer accuracy and contextual relevance.

Advanced RAG techniques are introduced in depth, including self-querying retrieval, parent document retrieval, hybrid search using BM25 and ensemble methods, contextual compression, filtering, HyDE (Hypothetical Document Embeddings), and RAG Fusion. Learners also explore modern tools such as Gemini RAG file search for scalable document retrieval.

By the end of the course, learners will be able to design robust, efficient, and customizable RAG systems suitable for enterprise search, private knowledge bases, and AI assistants.