Is this course suitable for beginners?

Yes, it’s designed for beginners with clear explanations, demos, and step-by-step practice.

Will there be hands-on exercises or projects?

Yes, you’ll complete coding exercises, practical demos, and a real-world capstone project.

What tools or libraries will I use during the course?

You’ll use LangChain, FAISS, Pydantic, and Streamlit for development and deployment.

Can I access the course content after completion?

Yes, you’ll have continued access to all course materials even after completion.

Are there any quizzes or assessments included?

Yes, each module includes graded quizzes, practice exercises, and final assessments.

Will I receive a certificate after completing the course?

Yes, you’ll earn a verified certificate upon successfully completing all modules and the capstone.

How does this course help in deploying real-world LLM models?

It teaches you to design, evaluate, and deploy production-ready AI applications using LangChain and LCEL.

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.

Developing LLM Applications with LangChain

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Developing LLM Applications with LangChain

本课程是 Building LLMs with Hugging Face and LangChain 专项课程的一部分

位教师：Edureka

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本课程是 Building LLMs with Hugging Face and LangChain 专项课程专项课程的一部分

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该课程共有4个模块

This course introduces the concepts, tools, and practical techniques behind LangChain, the leading framework for building intelligent applications powered by Large Language Models (LLMs). It blends conceptual understanding with hands-on implementation to help you design, build, and deploy context-aware, tool-using AI systems.

Whether you’re a developer, data scientist, or AI practitioner, this course provides a clear roadmap for transforming LLMs into dynamic, reasoning-driven applications that interact with real-world data and APIs. Through guided lessons, structured demonstrations, and project-based learning, you’ll explore how LangChain connects prompts, models, memory, and tools into composable workflows. You’ll learn to build Retrieval-Augmented Generation (RAG) pipelines, integrate LangServe for deployment, and implement LangSmith for observability and evaluation. The course culminates with a capstone Knowledge Assistant project, where you’ll combine RAG, multi-agent systems, and secure API integrations into a fully functional, deployable AI assistant. By the end of this course, you will be able to: • Understand the architecture and components of LangChain for LLM development. • Build multi-step reasoning pipelines and retrieval-augmented generation (RAG) workflows. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Evaluate and optimize LLM applications for performance, safety, and scalability. This course is ideal for AI developers, data scientists, and software engineers seeking to go beyond prompt-based experimentation and build real-world, production-ready LLM applications. A working knowledge of Python and APIs is recommended, but the course provides guided support to help learners of all backgrounds master the LangChain ecosystem. Join us to master the framework that powers today’s most advanced generative AI applications.

Learn the foundations of LangChain and its Expression Language (LCEL) for building modular, composable LLM workflows. This module covers core components such as prompt templates, memory, and chain composition, enabling learners to design structured reasoning pipelines and create their first multi-step LLM chain.

涵盖的内容

15个视频5篇阅读材料4个作业1个讨论话题

15个视频总计66分钟

Specialization Introduction6分钟
Course Introduction4分钟
What Is LangChain and how it works with LLMs and LCEL3分钟
Understanding the LangChain Workflow and Architecture3分钟
Demonstration: Installing and Setting Up LangChain Environment (Colab + API Keys)3分钟
Demonstration: Building Your First LLM Chain Using LCEL5分钟
Prompt Templates and Memory in LangChain3分钟
Designing Adaptive Prompt Templates for Context Control and Validation3分钟
Demonstration: Adding Conversation Memory to an LLM Chat Chain4分钟
Demonstration: Integrating Pydantic with LCEL for Structured Outputs6分钟
Understanding LCEL and Runnables in LangChain3分钟
Chain Types in LangChain — Sequential, Router and Custom3分钟
Designing Modular Reasoning Workflows in LangChain3分钟
Demonstration: Building a Multi-Step Reasoning Chain for QnA (LCEL)5分钟
Demonstration: Converting Chains to LCEL-Runnables for Efficient Execution4分钟

5篇阅读材料总计75分钟

Welcome to Developing LLM Applications and LangChain15分钟
LangChain Overview, LCEL Basics and Key Concepts15分钟
Reading: Prompt Design Patterns and Context Retention Techniques15分钟
Best Practices for Chain Composition and LCEL Integration15分钟
Summary of LangChain Fundamentals15分钟

4个作业总计48分钟

Knowledge Check: LangChain Fundamentals30分钟
Introduction to LangChain - Setup and Core Concepts6分钟
Practice Quiz : Designing Dynamic AI Contexts6分钟
Practice Quiz : Building and Combining Chains with LCEL/Runnables6分钟

1个讨论话题总计10分钟

Introduce Yourself10分钟

Explore Retrieval-Augmented Generation (RAG) to connect LLMs with external knowledge sources. Learners will build document ingestion and validation pipelines, create embeddings, and evaluate retrieval workflows using LangSmith. By the end, you’ll construct a retrieval-based Q&A system powered by LangChain.

涵盖的内容

12个视频4篇阅读材料4个作业

12个视频总计55分钟

Why Context Matters in LLM Responses3分钟
Embeddings, Vectors and LCEL in RAG 3分钟
Demonstration: Creating Text Embeddings Using Hugging Face or OpenAI Models5分钟
Demonstration: Setting Up a Vector Store with FAISS4分钟
Working with Document Loaders and Text Splitters4分钟
Document Processing and Validation Workflow in LangChain3分钟
Demonstration: Loading and Splitting Text Files for Indexing5分钟
Demonstration: Validating Documents with Pydantic and LCEL6分钟
Designing the Retrieval Workflow3分钟
Observability and Evaluation in RAG Pipelines Using LangSmith3分钟
Demonstration: Building Vector Stores to Retreiver Chain6分钟
Demonstration: Querying Your Documents with a Context-Aware LLM 6分钟

4篇阅读材料总计60分钟

Overview of RAG Architecture and LCEL for RAG15分钟
LangChain Document Loader Reference and Validation Patterns15分钟
Evaluating RAG Performance, Observability with LangSmith15分钟
Summary of Building Context-Aware Applications - RAG and Document Pipelines15分钟

4个作业总计48分钟

Knowledge Check: Building Context-Aware Applications - RAG and Document Pipelines30分钟
Practice Quiz : Retrieval-Augmented Generation Concepts6分钟
Parctice Quiz: Loading, Preprocessing, and Validating Documents6分钟
Practice Quiz : Building and Evaluating a Retrieval Pipeline6分钟

Discover how to build dynamic, decision-making AI systems using LangChain agents and LangServe. This module focuses on creating tool-using agents, integrating secure APIs, and deploying workflows as production-ready services. Learners will complete the capstone Knowledge Assistant, combining chains, RAG, and multi-agent communication protocols.

涵盖的内容

15个视频4篇阅读材料4个作业

15个视频总计76分钟

What Are Agents and How They Use Tools3分钟
Deploying and Managing Agents via LangServe APIs3分钟
Demonstration: Creating a Simple Tool-Using Agent6分钟
Demonstration: Adding Your RAG Pipeline as a Tool 6分钟
LangGraph Overview — Connecting Agents with Workflow Graphs (Brief Introduction)3分钟
Connecting APIs and Tracking Agents with LangSmith3分钟
Demonstration: Connecting to a Weather API for Real-Time Updates6分钟
Demonstration: Building Smarter Agents with Memory and Decision Flow - I4分钟
Demonstration: Building Smarter Agents with Memory and Decision Flow - II3分钟
Capstone Overview and Architecture Planning4分钟
Demonstration: Building AI Financial Agents with Python6分钟
Demonstration: Advanced Tools for AI Trading Systems6分钟
Demonstration: AI Trading Agents in Action7分钟
Demonstration: AI Orchestration with Python3分钟
Demonstration: Interactive AI Financial Dashboard with Streamlit 7分钟

4篇阅读材料总计60分钟

LangChain Agent Types, LangServe Concepts15分钟
Secure API Integration, Key Management, and Observability with LangSmith15分钟
MCP, ACP, ANP, and A2A Protocol Reference Notes15分钟
Summary of Connecting Agents and Tools15分钟

4个作业总计48分钟

Knowledge Check: Connecting Agents and Tools30分钟
Practice Quiz : Understanding LangChain Agents and LangServe6分钟
Practice Quiz : Integrating APIs, LangGraph, and Observability6分钟
Practice Quiz : Building and Testing Your Knowledge Assistant6分钟

Deploy, refine, and optimize your multi-agent Knowledge Assistant for real-world use. This module emphasizes fine-tuning, performance monitoring, and best practices for scalable LangServe deployments. Learners reflect on their project, review key takeaways, and prepare for advanced experimentation with custom and fine-tuned LLMs.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题

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常见问题

Basic Python knowledge and a general understanding of Large Language Models are recommended.

The course covers LangChain, LCEL, RAG pipelines, agents, and a full capstone project.

It can be completed in 4–6 weeks with around 3–5 hours of weekly learning.

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.