What are the prerequisites for this course?

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

What topics are covered in the course?

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

How long is the course duration?

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

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.

When will I have access to the lectures and assignments?

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.

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

This course is part of Building LLMs with Hugging Face and LangChain Specialization

Instructor: Edureka

Included with

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4 modules

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

4 modules

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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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.

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 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.

Module details

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 videosTotal 67 minutes

Specialization Introduction6 minutes
Course Introduction5 minutes
What Is LangChain and how it works with LLMs and LCEL4 minutes
Understanding the LangChain Workflow and Architecture4 minutes
Demonstration: Installing and Setting Up LangChain Environment (Colab + API Keys)4 minutes
Demonstration: Building Your First LLM Chain Using LCEL6 minutes
Prompt Templates and Memory in LangChain4 minutes
Designing Adaptive Prompt Templates for Context Control and Validation4 minutes
Demonstration: Adding Conversation Memory to an LLM Chat Chain5 minutes
Demonstration: Integrating Pydantic with LCEL for Structured Outputs6 minutes
Understanding LCEL and Runnables in LangChain4 minutes
Chain Types in LangChain — Sequential, Router and Custom4 minutes
Designing Modular Reasoning Workflows in LangChain4 minutes
Demonstration: Building a Multi-Step Reasoning Chain for QnA (LCEL)5 minutes
Demonstration: Converting Chains to LCEL-Runnables for Efficient Execution5 minutes

5 readingsTotal 75 minutes

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

4 assignmentsTotal 48 minutes

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

1 discussion promptTotal 10 minutes

Introduce Yourself10 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 videosTotal 56 minutes

Why Context Matters in LLM Responses4 minutes
Embeddings, Vectors and LCEL in RAG 4 minutes
Demonstration: Creating Text Embeddings Using Hugging Face or OpenAI Models5 minutes
Demonstration: Setting Up a Vector Store with FAISS5 minutes
Working with Document Loaders and Text Splitters4 minutes
Document Processing and Validation Workflow in LangChain3 minutes
Demonstration: Loading and Splitting Text Files for Indexing5 minutes
Demonstration: Validating Documents with Pydantic and LCEL6 minutes
Designing the Retrieval Workflow3 minutes
Observability and Evaluation in RAG Pipelines Using LangSmith3 minutes
Demonstration: Building Vector Stores to Retreiver Chain6 minutes
Demonstration: Querying Your Documents with a Context-Aware LLM 6 minutes

4 readingsTotal 60 minutes

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

4 assignmentsTotal 48 minutes

Knowledge Check: Building Context-Aware Applications - RAG and Document Pipelines30 minutes
Practice Quiz : Retrieval-Augmented Generation Concepts6 minutes
Parctice Quiz: Loading, Preprocessing, and Validating Documents6 minutes
Practice Quiz : Building and Evaluating a Retrieval Pipeline6 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 videosTotal 77 minutes

What Are Agents and How They Use Tools3 minutes
Deploying and Managing Agents via LangServe APIs3 minutes
Demonstration: Creating a Simple Tool-Using Agent6 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 LangSmith3 minutes
Demonstration: Connecting to a Weather API for Real-Time Updates6 minutes
Demonstration: Building Smarter Agents with Memory and Decision Flow - I5 minutes
Demonstration: Building Smarter Agents with Memory and Decision Flow - II4 minutes
Capstone Overview and Architecture Planning4 minutes
Demonstration: Building AI Financial Agents with Python7 minutes
Demonstration: Advanced Tools for AI Trading Systems6 minutes
Demonstration: AI Trading Agents in Action7 minutes
Demonstration: AI Orchestration with Python4 minutes
Demonstration: Interactive AI Financial Dashboard with Streamlit 7 minutes

4 readingsTotal 60 minutes

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

4 assignmentsTotal 48 minutes

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