Unleash the Power of Large Language Models Using LangChain Course Syllabus

Overview: This concise, hands-on course guides you through the core concepts and practical applications of LangChain, enabling you to build and deploy powerful large language model (LLM) workflows in just two hours. With a fully interactive, in-browser coding environment, you’ll progress from foundational components to advanced multi-agent systems, reinforced by real-time feedback and a final project. Perfect for developers and data scientists looking to rapidly prototype LLM-powered applications.

Module 1: Introduction to LangChain

Estimated time: 0.4 hours

• What Is a Language Model?
• What Is LangChain and Why Does It Matter?
• Use Cases of LangChain

Module 2: Exploring LangChain

Estimated time: 0.8 hours

• Chat Models, Messages, and Prompt Templates
• Parsing Outputs
• Runnables & Expression Language
• Tools
• Embeddings & Vector Stores

Module 3: LangGraph Basics

Estimated time: 0.8 hours

• What Is LangGraph?
• Main Components of LangGraph
• Why Traditional Chains Fall Short
• How to Create a Routing System

Module 4: Wrapping Up

Estimated time: 0.2 hours

• Integrating LangChain with LLMs
• Dynamic agents
• Future possibilities

Module 5: Final Project

Estimated time: 0.3 hours

• Query CSV Files with Natural Language Using LangChain and Panel
• Build a natural language interface for data querying
• Evaluate multi-agent workflow performance

Prerequisites

• Basic understanding of Python programming
• Familiarity with artificial intelligence concepts
• Experience with command-line tools (helpful but not required)

What You'll Be Able to Do After

• Understand the core architecture of LangChain and its components
• Create and manage prompt templates and parse LLM outputs effectively
• Integrate external tools and services into LLM workflows
• Generate and query embeddings using vector databases
• Design dynamic multi-agent systems using LangGraph for complex routing