LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps) Course Syllabus

Overview: This hands-on course guides engineers through building real-world LLM applications using LangChain v0.3+. Over approximately 7 hours of content, you'll construct three full pipelines—an intelligent agent, a documentation chatbot with RAG, and a code interpreter clone—while mastering prompt engineering, memory, vector stores, and advanced LangChain concepts. Updated in June 2025, the course includes modern features like Model Context Protocol (MCP), LangSmith, and an introduction to LangGraph. Each module blends practical coding with deep dives into internals for a comprehensive understanding.

Module 1: Introduction & Setup

Estimated time: 0.5 hours

• Install Python and set up the development environment
• Install LangChain (v0.3+) and required dependencies
• Configure OpenAI and Pinecone API keys
• Understand LangChain architecture and core components

Module 2: Build an Ice‑Breaker Agent

Estimated time: 2 hours

• Design an agent to scrape LinkedIn/Twitter profiles
• Implement function-calling for external tasks
• Integrate Chains and Toolkits for agent workflows
• Generate personalized ice-breaker messages using LLMs

Module 3: Documentation Chatbot

Estimated time: 1.5 hours

• Use DocumentLoader to ingest Python package documentation
• Apply TextSplitter and create embeddings
• Store vectors in FAISS and Pinecone
• Build a RAG-powered chatbot with memory and streaming

Module 4: Code Interpreter Chat Clone

Estimated time: 1.5 hours

• Build a lightweight code interpreter with LangChain
• Enable streaming code execution and file operations
• Integrate embedded agents for dynamic code handling
• Fine-tune prompt templates for robust code generation

Module 5: Prompt Engineering & Theory

Estimated time: 1 hour

• Master Chain-of-Thought, ReAct, and Few-Shot prompting
• Implement output parsing techniques in LangChain
• Explore Model Context Protocol (MCP) and LangSmith
• Introduction to LangGraph for stateful workflows

Module 6: Debug, Extend & Best Practices

Estimated time: 1 hour

• Debug complex agent behaviors and failures
• Add UI support using Streamlit
• Walk through LangChain internals and unit testing
• Apply tool chaining strategies and model refinement

Prerequisites

• Strong working knowledge of Python programming
• Familiarity with APIs and command-line tools
• Basic understanding of machine learning and LLMs

What You'll Be Able to Do After

• Build and deploy agent-based LLM applications
• Design and implement RAG pipelines with vector storage
• Develop interactive code interpreters with streaming I/O
• Apply advanced prompt engineering and parsing techniques
• Use LangSmith and MCP for observability and tooling