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.
LangChain Course for LLM Application Development
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LangChain Course for LLM Application Development
This course is part of LLM Application Engineering and Development Certification Specialization
Instructor: Priyanka Mehta
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Use LangChain document loaders, text splitters, and parsers for processing unstructured data
Implement embeddings and vector stores to enable semantic search and retrieval
Build advanced workflows with LangChain chains like Sequential and Map Reduce
Create dynamic, context-aware applications using memory and agent components
Skills you'll gain
Details to know
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Recently updated!
July 2025
Assessments
13 assignments
Taught in English
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This course is part of the LLM Application Engineering and Development Certification Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 4 modules in this course
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.
What's included
8 videos1 reading4 assignments
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.
What's included
4 videos3 assignments
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.
What's included
5 videos3 assignments
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.
What's included
6 videos3 assignments
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Frequently asked questions
To build LLM applications with LangChain, you use its modular components like prompts, chains, memory, and agents to connect language models with tools, documents, and APIs. LangChain enables context-aware, multi-step reasoning in your applications.
The best LLM course covers foundational concepts, prompt engineering, model integration (like GPT or Flan T5), and hands-on tools such as LangChain or Hugging Face. Look for practical projects that demonstrate real-world use cases.
A LangChain course teaches how to use the LangChain framework to build generative AI workflows and applications using large language models. It covers components like chains, memory, embeddings, and agents to create intelligent, scalable solutions.
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