LangChain Course for LLM Application Development

Overview

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.

Syllabus

Foundations of Model I/O and Document Processing

Explore the foundations of Model I/O and document processing in LangChain. Learn how prompts, language models, and output parsers interact within chatbot workflows. Understand how to use document loaders and text splitters to process unstructured data. Gain hands-on experience with LangChain components through demos covering document types, loading strategies, and text splitting methods.

Embeddings and Vector Stores

Learn how embeddings and vector stores power search and retrieval in Generative AI applications. Explore the fundamentals of embeddings, their role in representing text, and how they connect to vector databases. Understand how to use text embedding models and VectorStore for efficient data querying. Get hands-on with LangChain demos using loaders, text splitters, and embeddings.

LangChain Retrieval and Chains

Master LangChain Retrieval and Chains to enhance your Generative AI workflows. Learn how LangChain Retrievers locate relevant data and how different chain types such as Sequential, Stuff, Refine, and Map Reduce to process and manage information. Explore real-world applications with demos, including how to build Sequential Chains for streamlined AI-driven task execution.

LangChain Memory and Agents

Explore LangChain Memory and Agents to build dynamic, context-aware GenAI applications. Learn the types of memory in LangChain and how they enable conversational continuity. Understand how agents make decisions and interact with tools. Gain hands-on experience creating LangChain agents, using memory, and running local Falcon LLM models in real-world AI workflows.