Learn the fundamentals of Retrieval-Augmented Generation (RAG) and why it’s critical for reducing hallucinations and improving accuracy. You’ll break down RAG’s core components, retrievers, rankers, generators, and orchestration layers, and apply design patterns for use cases like Q&A, summarization, and knowledge synthesis. You’ll also explore advanced variations such as hierarchical retrieval and hybrid search, giving you practical strategies to match RAG designs to real-world needs.

Evaluate embedding models and vector databases to understand how they impact retrieval quality and system performance. You’ll compare embedding options by dimensionality and domain fit, and explore database choices such as Facebook AI Similarity Search (FAISS), ChromaDB, Milvus, and Pinecone. You’ll also analyze indexing strategies, chunking methods, and update workflows—skills that help you make informed decisions when building retrieval systems for different environments.

You’ll put theory into practice by integrating embeddings and vector databases into working RAG pipelines. You’ll test indexing strategies, experiment with chunking, and observe how different configurations affect retrieval accuracy and efficiency. You’ll also practice maintaining and updating vector indices, building the skills to manage RAG systems that remain reliable as datasets grow and change.

Assemble full RAG pipelines using frameworks like LangChain and open Large Language Models (LLMs). You’ll implement advanced retrieval strategies such as hybrid search, re-ranking, and query expansion, and evaluate pipelines with metrics that track accuracy, latency, and reliability. You’ll also practice handling real-world challenges, such as hallucination mitigation and citation tracking, ensuring your systems are not just demos, but production-ready solutions.