Building RAG Applications with Vector Databases

As artificial intelligence evolves, large language models need access to external data to provide accurate, up-to-date answers. Retrieval-Augmented Generation (RAG) solves this by connecting models to vector databases, making it one of the most critical skills in modern AI development.\n\nIn this text-based course, you will transition from understanding basic database concepts to constructing efficient retrieval systems. You will learn how to convert text into vector embeddings, store them in specialized databases, and retrieve the most relevant information to ground your AI responses in real-world facts.\n\nWhat you'll learn:\n- Understand the core concepts of semantic search, embeddings, and the RAG architecture.\n- Store and query high-dimensional data using vector databases like Chroma and FAISS.\n- Implement document chunking strategies to optimize retrieval accuracy.\n- Connect language models with external data sources using LangChain and LlamaIndex.\n- Evaluate retrieval quality and implement basic semantic caching to improve performance.\n- Build simple text-based search interfaces to interact with your knowledge base.\n\nYou will start with foundational definitions of embeddings and vector spaces before moving on to hands-on configuration of vector stores. Through clear written explanations and structured code walkthroughs, you will progress to building fully functional retrieval pipelines.\n\nThis course is designed for beginner developers, data enthusiasts, and software engineers who want to learn the fundamentals of modern AI search systems. No prior experience with vector databases or machine learning is required, though a basic understanding of Python is helpful.\n\nStart reading today to master the core components of modern retrieval-augmented generation.