Building QA and RAG Systems with LangChain and Custom Data

Generative language models become dramatically more effective when grounded in private documents and domain-specific knowledge. This text-based course guides you through creating reliable question-answering workflows and retrieval-augmented generation (RAG) architectures using LangChain. What you'll learn: - Understand core LangChain components, document loaders, and text chunking strategies - Create vector embeddings and store them in vector databases for efficient similarity search - Build customized question-answering chains to query your own text files and datasets - Apply prompt templates and context injection techniques to reduce model hallucination - Configure multi-step retrieval-augmented generation pipelines for specialized data - Practice evaluating retrieval accuracy and managing token context constraints The course begins by clarifying essential terminology, embeddings, and vector search fundamentals before walking through detailed code examples and end-to-end implementation patterns. It is designed for beginners and developers with basic programming knowledge looking to work with retrieval workflows. Start reading today to bring your own data into modern generative AI applications.