What are the prerequisites to learn LangChain?

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

What is the difference between LangChain and LLM?

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.

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

LangChain Course for LLM Application Development

LangChain Course for LLM Application Development

本课程是 LLM Application Engineering and Development Certification 专项课程的一部分

位教师：Priyanka Mehta

包含在 Coursera Plus 中

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4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

要了解的详细信息

可分享的证书

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 LLM Application Engineering and Development Certification 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

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.

涵盖的内容

8个视频1篇阅读材料4个作业

8个视频总计55分钟

Learning Objectives4分钟
Flow of Chatbot Application and Model I/O6分钟
Demo: LangChain-Models, Prompts, and Output Parsers23分钟
Chatbot Application Flow and Document Loaders2分钟
Types of Document Loaders: Part 12分钟
Types of Document Loaders: Part 25分钟
Text Splitters and Its Examples6分钟
Text Splitters: Recursive Character Text Splitter2分钟

1篇阅读材料总计10分钟

Course Syllabus 10分钟

4个作业总计85分钟

Quiz on Model I/O: Prompts, Language Models, and Parsers15分钟
Quiz on Document Loaders15分钟
Quiz on Text Splitters15分钟
Assessment for Foundations of Model I/O and Document Processing40分钟

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.

涵盖的内容

4个视频3个作业

4个视频总计33分钟

Introduction to Embeddings in GenAI6分钟
Text Embedding Models: Intuition and Examples6分钟
Overview of VectorStore and Its Working5分钟
Demo: Loaders, Text Splitters, Embeddings, and Vector Stores14分钟

3个作业总计70分钟

Quiz on Embeddings in GenAI15分钟
Quiz on Introduction to Vector Store15分钟
Assessment for Embeddings and Vector Stores40分钟

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.

涵盖的内容

5个视频3个作业

5个视频总计36分钟

Overview and Examples of LangChain Retriever5分钟
Introduction and LangChain Chains and Its Framework6分钟
Sequential Chain and Stuff Chain in LangChain6分钟
Refine Chain and Map Reduce Chain in LangChain4分钟
Demo: LangChain Sequential Chain12分钟

3个作业总计70分钟

Quiz on LangChain Retriever15分钟
Quiz on LangChain Chains15分钟
Assessment for LangChain Retrieval and Chains40分钟

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.

涵盖的内容

6个视频3个作业

6个视频总计55分钟

Introduction to LangChain Memory and Its Types4分钟
Demo: Langchain Memory6分钟
LangChain Agents and Chat Messages6分钟
Demo: Creating and Utilizing LangChain Agents19分钟
Demo: Running Local Falcon LLM16分钟
Key Takeaways2分钟

3个作业总计70分钟

Quiz on LangChain Memory15分钟
Quiz on Introduction to LangChain Agents15分钟
Assessment for LangChain Memory and Agents40分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Priyanka Mehta

Simplilearn

62 门课程25,934 名学生

提供方

Simplilearn

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常见问题

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 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.