LangChain for LLM Application Development - Certification Journey

🎯 Purpose

This repository documents my journey through the LangChain for LLM Application Development course by DeepLearning.AI, taught by Harrison Chase (LangChain Co-Founder/CEO) and Andrew Ng. The goal is to gain certification and build practical skills to become an AI Engineer.

What You'll Learn

• Expand LLM capabilities using the LangChain framework
• Apply LLMs to proprietary data and build personal assistants
• Create specialized chatbots with memory and context
• Use agents, chained calls, and memories effectively
• Build production-ready LLM applications

🗂️ Repository Structure

langchain-certification/
├── README.md                    # This file
├── .env.example                # Template for API keys
├── lesson/                     # Course materials and notes
│   ├── L1-Model_prompt_parser.py  # Lesson 1: Models, Prompts and Parsers
│   ├── L2-Memory.py           # Lesson 2: Memory
│   └── README.md              # Lesson descriptions
├── projects/                   # Practical implementations
│   ├── lesson1_primal_tcg/   # Real-world application of Lesson 1
│   │   ├── primal_tcg_rules_assistant.py  # Main implementation
│   │   ├── test_primal_assistant.py       # Testing without API
│   │   ├── README_primal_tcg.md          # Project documentation
│   │   └── venv/                          # Virtual environment
│   └── lesson2_memory_trading/ # Real-world application of Lesson 2
│       └── README.md          # Project documentation
└── docs/                      # Additional documentation
    └── Primal_TCG_Comprehensive_Rules_V1.3.1.md

📖 Course Syllabus & Progress

✅ Lesson 1: Introduction (3 mins)

• Course overview and objectives

✅ Lesson 2: Models, Prompts and Parsers (18 mins)

• Status: Completed
• Topics:
  • Direct API calls to OpenAI
  • Prompt templates with LangChain
  • Output parsers for structured data
• Project: Primal TCG Rules Assistant
  • Built a rules clarification system for trading card game
  • Implemented structured output parsing for analytics
  • Created chained prompts for complex interactions

✅ Lesson 3: Memory (17 mins)

• Status: Completed
• Topics:
  • ConversationBufferMemory for full history
  • ConversationSummaryMemory for condensed context
  • ConversationBufferWindowMemory for recent messages
  • Memory integration with chains
• Project: Memory Trading Assistant
  • Built a trading assistant with persistent memory
  • Implemented context-aware market analysis
  • Created conversational trading strategy discussions

⏳ Lesson 4: Chains (13 mins)

• Status: Upcoming
• Topics: Creating sequences of operations

⏳ Lesson 5: Question and Answer (15 mins)

• Status: Upcoming
• Topics: Applying LLMs to proprietary data

⏳ Lesson 6: Evaluation (15 mins)

• Status: Upcoming
• Topics: Testing and evaluating LLM applications

⏳ Lesson 7: Agents (14 mins)

• Status: Upcoming
• Topics: LLMs as reasoning agents

⏳ Lesson 8: Conclusion (1 min)

• Status: Upcoming
• Topics: Course wrap-up and next steps

🛠️ Technologies Used

• Python 3.13+
• LangChain - Core framework for LLM applications
• OpenAI API - Language model provider
• langchain-openai - OpenAI integration for LangChain
• python-dotenv - Environment variable management

🚀 Getting Started

Prerequisites

1. Python 3.7+ installed
2. OpenAI API key (Get one here)

Installation

# Clone the repository
git clone <repository-url>
cd langchain-certification

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install python-dotenv openai langchain langchain-openai langchain-community

# Set up environment variables
cp .env.example .env
# Edit .env and add your OpenAI API key

Running Projects

Each lesson has its own project directory. For example:

# Navigate to a project
cd projects/lesson1_primal_tcg

# Activate virtual environment
source venv/bin/activate

# Run the project
python primal_tcg_rules_assistant.py

💡 Key Learning Outcomes

From Lesson 1: Models, Prompts and Parsers

• ✅ Direct API calls vs LangChain abstractions
• ✅ Creating reusable prompt templates
• ✅ Parsing LLM outputs into structured JSON
• ✅ Building production-ready applications with error handling
• ✅ Implementing analytics and pattern tracking

From Lesson 2: Memory

• ✅ Implementing conversation memory in applications
• ✅ Managing different memory types for optimal performance
• ✅ Building context-aware chatbots
• ✅ Persisting conversation history across sessions
• ✅ Memory optimization strategies for long conversations

🎓 Certification Goal

Upon completion of this course, I aim to:

1. Gain DeepLearning.AI certification in LangChain development
2. Build a portfolio of practical LLM applications
3. Develop skills required for AI Engineering roles
4. Master LangChain framework for production use

📈 Progress Tracking

• Course Started: August 2025
• Lessons Completed: 2/8
• Projects Built: 2
• Lines of Code: 800+
• API Integrations: OpenAI

🔗 Resources

• Course Link
• LangChain Documentation
• OpenAI API Documentation
• DeepLearning.AI

📝 Notes

This repository serves as both a learning journal and a practical implementation guide. Each project demonstrates real-world applications of the concepts learned, going beyond simple tutorials to create functional, production-ready systems.

🤝 Acknowledgments

• Harrison Chase - LangChain Co-Founder/CEO & Course Instructor
• Andrew Ng - DeepLearning.AI Founder & Course Instructor
• DeepLearning.AI - For providing this comprehensive course

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This repository is part of my journey to become an AI Engineer, focusing on practical applications of LLMs using the LangChain framework.