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⇱ LangChain Course for LLM Application Development | Coursera

LangChain Course for LLM Application Development

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LangChain Course for LLM Application Development

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
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

Details to know

Shareable certificate

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Assessments

13 assignments

Taught in English

Build your subject-matter expertise

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

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

8 videos•Total 55 minutes
  • Learning Objectives•5 minutes
  • Flow of Chatbot Application and Model I/O•7 minutes
  • Demo: LangChain-Models, Prompts, and Output Parsers•24 minutes
  • Chatbot Application Flow and Document Loaders•2 minutes
  • Types of Document Loaders: Part 1•3 minutes
  • Types of Document Loaders: Part 2•6 minutes
  • Text Splitters and Its Examples•6 minutes
  • Text Splitters: Recursive Character Text Splitter•3 minutes
1 reading•Total 10 minutes
  • Course Syllabus •10 minutes
4 assignments•Total 85 minutes
  • Quiz on Model I/O: Prompts, Language Models, and Parsers•15 minutes
  • Quiz on Document Loaders•15 minutes
  • Quiz on Text Splitters•15 minutes
  • Assessment for Foundations of Model I/O and Document Processing•40 minutes

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

4 videos•Total 34 minutes
  • Introduction to Embeddings in GenAI•6 minutes
  • Text Embedding Models: Intuition and Examples•7 minutes
  • Overview of VectorStore and Its Working•6 minutes
  • Demo: Loaders, Text Splitters, Embeddings, and Vector Stores•14 minutes
3 assignments•Total 70 minutes
  • Quiz on Embeddings in GenAI•15 minutes
  • Quiz on Introduction to Vector Store•15 minutes
  • Assessment for Embeddings and Vector Stores•40 minutes

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

5 videos•Total 37 minutes
  • Overview and Examples of LangChain Retriever•5 minutes
  • Introduction and LangChain Chains and Its Framework•7 minutes
  • Sequential Chain and Stuff Chain in LangChain•7 minutes
  • Refine Chain and Map Reduce Chain in LangChain•5 minutes
  • Demo: LangChain Sequential Chain•13 minutes
3 assignments•Total 70 minutes
  • Quiz on LangChain Retriever•15 minutes
  • Quiz on LangChain Chains•15 minutes
  • Assessment for LangChain Retrieval and Chains•40 minutes

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

6 videos•Total 56 minutes
  • Introduction to LangChain Memory and Its Types•5 minutes
  • Demo: Langchain Memory•6 minutes
  • LangChain Agents and Chat Messages•7 minutes
  • Demo: Creating and Utilizing LangChain Agents•19 minutes
  • Demo: Running Local Falcon LLM•16 minutes
  • Key Takeaways•2 minutes
3 assignments•Total 70 minutes
  • Quiz on LangChain Memory•15 minutes
  • Quiz on Introduction to LangChain Agents•15 minutes
  • Assessment for LangChain Memory and Agents•40 minutes

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Instructor

Simplilearn
87 Courses•77,755 learners

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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.

To learn LangChain effectively, you should have a basic understanding of Python, APIs, and foundational knowledge of large language models or prompt engineering.

LangChain is a framework that helps developers build applications with LLMs (Large Language Models). While LLMs generate text or code, LangChain orchestrates how those models interact with data, memory, tools, and user input.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,