Building Generative AI Applications with LangChain and RAG

Generative AI is transforming how we interact with data, but generic models often lack the specific context needed for real-world tasks. Building Retrieval-Augmented Generation (RAG) applications allows you to connect powerful language models to your own private data sources securely and efficiently.\n\nThis text-based course guides you step-by-step through the process of designing, coding, and refining a complete RAG application. You will transition from understanding core artificial intelligence concepts to implementing a fully functional, context-aware AI assistant using the popular LangChain framework.\n\nWhat you'll learn:\n- Understand the core principles of Generative AI, embeddings, and vector search\n- Configure LangChain components to manage prompts, models, and memory\n- Load, chunk, and index external documents into a modern vector database\n- Construct a robust Retrieval-Augmented Generation (RAG) pipeline\n- Apply prompt engineering techniques to improve model accuracy and reduce hallucinations\n- Evaluate the performance of your RAG application using written test scenarios\n\nYou will start with foundational terminology and the mechanics of large language models, then progress through hands-on written tutorials that show you how to write clean, modular Python code using LangChain. Each section includes conceptual explanations and structured code walk-throughs to ensure you understand the logic behind every line.\n\nThis course is designed for software developers, data enthusiasts, and tech professionals who are new to Generative AI development. A basic understanding of Python is recommended, but no prior experience with AI frameworks or vector databases is required.\n\nStart reading today to build your first intelligent, data-connected AI application.