This LangChain for Advanced Generative AI Workflows course equips you with the skills to build scalable, retrieval-augmented applications using large language models. Begin with foundational concepts—learn how Model I/O, document loaders, and text splitters prepare and structure data for GenAI tasks. Progress to embedding techniques and vector stores for efficient semantic search and data retrieval. Master LangChain’s retrieval methods and chain types such as Sequential, Stuff, Refine, and Map Reduce to manage complex workflows. Conclude with LangChain Memory and Agents—develop context-aware systems and integrate local LLMs like Falcon for real-world applications.

To be successful in this course, you should have a solid understanding of Python, language models, and basic generative AI concepts. By the end of this course, you will be able to: - Structure and process unstructured data using LangChain I/O tools - Use embeddings and vector stores for semantic search and retrieval - Build multi-step GenAI workflows using LangChain chains and retrievers - Create context-aware applications with LangChain memory and agents Ideal for AI developers, ML engineers, and GenAI practitioners.