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Developing LLM Applications with LangChain

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Developing LLM Applications with LangChain

Instructor: Edureka

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

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the Building LLMs with Hugging Face and LangChain 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
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

What's included

15 videos5 readings4 assignments1 discussion prompt

15 videos•Total 67 minutes
  • Specialization Introduction•6 minutes
  • Course Introduction•5 minutes
  • What Is LangChain and how it works with LLMs and LCEL•4 minutes
  • Understanding the LangChain Workflow and Architecture•4 minutes
  • Demonstration: Installing and Setting Up LangChain Environment (Colab + API Keys)•4 minutes
  • Demonstration: Building Your First LLM Chain Using LCEL•6 minutes
  • Prompt Templates and Memory in LangChain•4 minutes
  • Designing Adaptive Prompt Templates for Context Control and Validation•4 minutes
  • Demonstration: Adding Conversation Memory to an LLM Chat Chain•5 minutes
  • Demonstration: Integrating Pydantic with LCEL for Structured Outputs•6 minutes
  • Understanding LCEL and Runnables in LangChain•4 minutes
  • Chain Types in LangChain — Sequential, Router and Custom•4 minutes
  • Designing Modular Reasoning Workflows in LangChain•4 minutes
  • Demonstration: Building a Multi-Step Reasoning Chain for QnA (LCEL)•5 minutes
  • Demonstration: Converting Chains to LCEL-Runnables for Efficient Execution•5 minutes
5 readings•Total 75 minutes
  • Welcome to Developing LLM Applications and LangChain•15 minutes
  • LangChain Overview, LCEL Basics and Key Concepts•15 minutes
  • Reading: Prompt Design Patterns and Context Retention Techniques•15 minutes
  • Best Practices for Chain Composition and LCEL Integration•15 minutes
  • Summary of LangChain Fundamentals•15 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: LangChain Fundamentals•30 minutes
  • Introduction to LangChain - Setup and Core Concepts•6 minutes
  • Practice Quiz : Designing Dynamic AI Contexts•6 minutes
  • Practice Quiz : Building and Combining Chains with LCEL/Runnables•6 minutes
1 discussion prompt•Total 10 minutes
  • Introduce Yourself•10 minutes

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.

What's included

12 videos4 readings4 assignments

12 videos•Total 56 minutes
  • Why Context Matters in LLM Responses•4 minutes
  • Embeddings, Vectors and LCEL in RAG •4 minutes
  • Demonstration: Creating Text Embeddings Using Hugging Face or OpenAI Models•5 minutes
  • Demonstration: Setting Up a Vector Store with FAISS•5 minutes
  • Working with Document Loaders and Text Splitters•4 minutes
  • Document Processing and Validation Workflow in LangChain•3 minutes
  • Demonstration: Loading and Splitting Text Files for Indexing•5 minutes
  • Demonstration: Validating Documents with Pydantic and LCEL•6 minutes
  • Designing the Retrieval Workflow•3 minutes
  • Observability and Evaluation in RAG Pipelines Using LangSmith•3 minutes
  • Demonstration: Building Vector Stores to Retreiver Chain•6 minutes
  • Demonstration: Querying Your Documents with a Context-Aware LLM •6 minutes
4 readings•Total 60 minutes
  • Overview of RAG Architecture and LCEL for RAG•15 minutes
  • LangChain Document Loader Reference and Validation Patterns•15 minutes
  • Evaluating RAG Performance, Observability with LangSmith•15 minutes
  • Summary of Building Context-Aware Applications - RAG and Document Pipelines•15 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Building Context-Aware Applications - RAG and Document Pipelines•30 minutes
  • Practice Quiz : Retrieval-Augmented Generation Concepts•6 minutes
  • Parctice Quiz: Loading, Preprocessing, and Validating Documents•6 minutes
  • Practice Quiz : Building and Evaluating a Retrieval Pipeline•6 minutes

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.

What's included

15 videos4 readings4 assignments

15 videos•Total 77 minutes
  • What Are Agents and How They Use Tools•3 minutes
  • Deploying and Managing Agents via LangServe APIs•3 minutes
  • Demonstration: Creating a Simple Tool-Using Agent•6 minutes
  • Demonstration: Adding Your RAG Pipeline as a Tool •7 minutes
  • LangGraph Overview — Connecting Agents with Workflow Graphs (Brief Introduction)•3 minutes
  • Connecting APIs and Tracking Agents with LangSmith•3 minutes
  • Demonstration: Connecting to a Weather API for Real-Time Updates•6 minutes
  • Demonstration: Building Smarter Agents with Memory and Decision Flow - I•5 minutes
  • Demonstration: Building Smarter Agents with Memory and Decision Flow - II•4 minutes
  • Capstone Overview and Architecture Planning•4 minutes
  • Demonstration: Building AI Financial Agents with Python•7 minutes
  • Demonstration: Advanced Tools for AI Trading Systems•6 minutes
  • Demonstration: AI Trading Agents in Action•7 minutes
  • Demonstration: AI Orchestration with Python•4 minutes
  • Demonstration: Interactive AI Financial Dashboard with Streamlit •7 minutes
4 readings•Total 60 minutes
  • LangChain Agent Types, LangServe Concepts•15 minutes
  • Secure API Integration, Key Management, and Observability with LangSmith•15 minutes
  • MCP, ACP, ANP, and A2A Protocol Reference Notes•15 minutes
  • Summary of Connecting Agents and Tools•15 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Connecting Agents and Tools•30 minutes
  • Practice Quiz : Understanding LangChain Agents and LangServe•6 minutes
  • Practice Quiz : Integrating APIs, LangGraph, and Observability•6 minutes
  • Practice Quiz : Building and Testing Your Knowledge Assistant•6 minutes

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.

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 3 minutes
  • Course Summary: Building LLM Applications with LangChain•3 minutes
1 reading•Total 30 minutes
  • Practice Project: RAG-Enhanced Financial Multi-Agent System Using MCP•30 minutes
1 assignment•Total 30 minutes
  • End Course Knowledge Check: Building LLM Applications with LangChain•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,285 learners

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Frequently asked questions

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.

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

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

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

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

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

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

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

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,