LangChain with Python Bootcamp Course Syllabus

Overview: This hands-on bootcamp is designed to take you from beginner to proficient in building real-world applications with LangChain and Python. The course spans approximately 6 hours of content, divided into six structured modules that progressively build your skills—from foundational components like prompt templates and document loaders to advanced topics like agents, memory, and deployment with LangSmith and LangGraph. Each module includes practical Jupyter notebooks and clean code examples to reinforce learning. By the end, you'll have the confidence and portfolio-ready projects to deploy LangChain-powered apps.

Module 1: Model I/O & Prompt Templates

Estimated time: 0.5 hours

• Interacting with different LLMs through LangChain's model I/O layer
• Creating reusable prompt templates for dynamic workflows
• Switching between LLM providers (OpenAI, Hugging Face) seamlessly
• Using LangChain abstractions to maintain core logic across models

Module 2: Document Loaders & Vector Databases

Estimated time: 1.5 hours

• Loading documents from PDFs, text files, and web sources
• Preprocessing text using built-in text splitters
• Integrating vector databases like ChromaDB for semantic search
• Building retrieval-augmented generation (RAG) pipelines

Module 3: Chains & Memory Management

Estimated time: 1.5 hours

• Constructing sequential and transform chains for complex workflows
• Implementing retrieval chains for context-aware responses
• Managing conversational state using memory modules
• Applying memory in chatbot applications for persistent context

Module 4: Agents & Tool Integration

Estimated time: 2 hours

• Designing custom agents for intelligent decision-making
• Equipping agents with function-calling and web scraping capabilities
• Integrating external tools and APIs into agent workflows
• Using agent routers for dynamic model and tool selection

Module 5: Output Parsing & Serialization

Estimated time: 1 hour

• Extracting structured data from LLM outputs
• Using output parsers with Pydantic models for type safety
• Serializing results for API integration and downstream processing

Module 6: Deployment, LangSmith & LangGraph

Estimated time: 1 hour

• Monitoring and debugging applications with LangSmith
• Orchestrating complex workflows using LangGraph
• Applying best practices for testing and production readiness

Prerequisites

• Strong foundational knowledge of Python programming
• Familiarity with APIs and basic web services
• Basic understanding of machine learning concepts (helpful but not required)

What You'll Be Able to Do After

• Build and deploy production-ready LangChain applications confidently
• Integrate vector databases to create powerful RAG systems
• Develop intelligent agents capable of automation and function calling
• Structure LLM outputs for reliable downstream use in APIs and apps
• Use LangSmith and LangGraph for monitoring, testing, and scaling AI workflows