LangChain- Develop LLM powered applications with LangChain Course Syllabus

An up-to-date and well-structured course for building real-world LangChain applications with end-to-end pipelines, memory, RAG, and agents. This course spans approximately 6.5 hours of on-demand video content, divided into six comprehensive modules. You’ll progress from foundational concepts to advanced integrations, building production-ready LLM applications using modern LangChain practices. Each module combines theory with hands-on coding, culminating in three full projects deployed to real-world environments.

Module 1: Introduction to LangChain & Model Setup

Estimated time: 0.5 hours

• Understand LangChain framework architecture and design philosophy
• Set up Python environment and required dependencies
• Configure API keys for OpenAI and other LLM providers
• Initialize and test first LLM connection using LangChain

Module 2: Chains, Prompt Templates & Basic Apps

Estimated time: 2 hours

• Learn the structure and function of LangChain chains
• Create and manage prompt templates for dynamic inputs
• Map inputs and outputs across chain components
• Build a complete LangChain application using OpenAI LLM

Module 3: Memory & Document Loaders

Estimated time: 1.5 hours

• Integrate memory to preserve conversation context across turns
• Use document loaders to ingest text from PDFs and plain text files
• Process and chunk documents for downstream use
• Connect loaded data to LLM workflows

Module 4: RAG & Vector Databases

Estimated time: 1.5 hours

• Implement Retrieval-Augmented Generation (RAG) pipelines
• Set up vector stores using Pinecone and FAISS
• Generate embeddings and perform semantic similarity search
• Integrate retrieval logic into LangChain applications

Module 5: Agents, Callbacks & LCEL

Estimated time: 1.5 hours

• Design autonomous agents capable of multi-step reasoning
• Enable agents to call external APIs and execute Python code
• Use callbacks to monitor and debug LangChain executions
• Explore LangChain Expression Language (LCEL) for pipeline definition

Module 6: Final Project

Estimated time: 1 hours

• Build and deploy a full RAG-powered document assistant
• Develop a memory-enabled chatbot with agent capabilities
• Debug, optimize, and extend production-ready LLM applications

Prerequisites

• Intermediate Python programming skills
• Familiarity with OpenAI API and LLM fundamentals
• Basic understanding of command-line and package management

What You'll Be Able to Do After

• Build end-to-end LangChain applications in Python
• Apply prompt engineering techniques like chain-of-thought and ReAct
• Implement RAG systems using vector databases like Pinecone and FAISS
• Design intelligent agents with memory and tool integration
• Deploy production-style LLM apps with debugging and optimization