LangChain Essentials TutorialCore Components Implementation Guide
Comprehensive LangChain tutorial for intermediate developers. Master core components including chains, memory, agents, and prompt templates. Implementation guide for building production-ready AI applications.
🎯 What You'll Learn in This LangChain Tutorial Series
LangChain Architecture & Setup
Understand LangChain's architecture and set up your development environment for building AI applications.
Topics covered:
- •LangChain core concepts
- •Setting up development environment
- •Understanding the architecture
- •Basic components overview
Prompt Templates & Output Parsers
Learn how to create dynamic prompt templates and parse structured outputs from LLMs with LangChain.
Topics covered:
- •Creating dynamic prompt templates
- •Variable substitution
- •Output parsers and structured data
- •Custom parser implementation
Chains - Building AI Workflows
Step-by-step tutorial on building complex AI workflows with LangChain chains and components.
Topics covered:
- •Understanding LangChain chains
- •Sequential and parallel chains
- •Custom chain creation
- •Error handling in chains
Memory & Conversation Management
How to implement LangChain memory systems - tutorial for building conversational AI with context.
Topics covered:
- •Types of memory in LangChain
- •Conversation buffer memory
- •Summary and entity memory
- •Custom memory implementations
Agents & Tools Integration
How to build LangChain agents - step-by-step guide to creating AI that uses tools autonomously.
Topics covered:
- •Understanding LangChain agents
- •Built-in and custom tools
- •Agent decision making
- •Real-world agent examples
Document Processing & Text Splitting
LangChain document processing tutorial - how to split text and prepare data for RAG systems.
Topics covered:
- •Document loaders
- •Text splitting strategies
- •Metadata extraction
- •Processing pipelines
Ready to continue your journey?
Next: RAG Applications