Adding Context to Large Language Models with ChromaDB

Large language models are incredibly powerful, but they lack knowledge of your specific business data and private documents. To build truly useful AI applications, you must learn how to feed the right information to the model at the right time. This text-only course guides you through the fundamentals of Retrieval-Augmented Generation (RAG) to connect external data sources directly to your AI workflows. By reading through clear explanations and structured code examples, you will master the mechanics of vector search and context injection. You will learn how to take raw text data, turn it into searchable vector embeddings, and retrieve the exact information needed to answer user queries accurately. What you'll learn: Understand the core concepts of vector embeddings, semantic search, and RAG architecture; Configure ChromaDB as a lightweight vector database to store and query text data; Practice document chunking and metadata strategies to optimize retrieval accuracy; Apply prompt engineering techniques to seamlessly merge retrieved context with LLM prompts; Build a practical context-infused application, simulating a specialized customer support assistant; Learn how to evaluate and refine the retrieved context to prevent AI hallucinations. We start with foundational definitions of vector spaces and database mechanics before moving into step-by-step implementation tutorials. This course is designed for beginners who have a basic grasp of Python and want to learn modern AI development practices. Start reading today to build smarter, context-aware AI applications with your own data.