RAG Application Development with LangChain and Vector Databases

Static AI models often lack the specific, up-to-date information required for specialized tasks, leading to inaccurate or irrelevant responses. Retrieval-Augmented Generation (RAG) solves this by grounding Large Language Models in your own data, ensuring every output is backed by a verifiable source. This course transforms your understanding of modern AI by teaching you how to build systems that can read, process, and query private datasets. You will move from learning basic terminology to implementing sophisticated retrieval pipelines that handle everything from web content to structured databases. What you'll learn: - Understand the fundamental components of RAG architecture and the role of embeddings. - Configure vector databases and indexing strategies for high-speed information retrieval. - Apply the LangChain framework to orchestrate complex data workflows and chains. - Implement advanced retrieval techniques such as query expansion and result re-ranking. - Practice prompt engineering and semantic caching to optimize model performance and costs. - Build logic for querying diverse data sources including SQL databases and unstructured text. - Learn modern evaluation patterns to measure and improve the accuracy of your AI system. The course begins with a deep dive into foundational concepts like vectorization and semantic search before guiding you through the practical logic of building production-ready pipelines. You will focus on written explanations and code-based logic to master the mechanics of AI retrieval. This course is designed for beginners and developers who want a clear, text-based path into AI engineering. No prior experience with vector stores or LLM orchestration is required. Start building your own data-driven AI applications today.