ChatGPT and LangChain: The Complete Developer’s Masterclass Course Syllabus

Overview: This comprehensive masterclass guides developers through building production-grade AI applications using ChatGPT and LangChain. From foundational pipelines to advanced distributed systems, the course covers real-world implementation of retrieval-augmented generation, PDF chatbots, custom plugins, and observability. With approximately 7 hours of focused content, students will gain hands-on experience in backend AI engineering, streaming architectures, and scalable LLM workflows.

Module 1: Intro & Setup

Estimated time: 0.5 hours

• Environment setup: Python, LangChain, and ChatGPT-4 API integration
• Introduction to LangChain components and core concepts
• Foundations of chains and conversational memory
• Feedback-driven refinement in text generation

Module 2: Chains & Pipelines

Estimated time: 1.5 hours

• Building LangChain pipelines with multiple components
• Integrating feedback logic into generation workflows
• Implementing semantic memory for context retention
• Introduction to Retrieval-Augmented Generation (RAG)
• Using embeddings and retrievers for dynamic context

Module 3: Retrieval-Augmented Generation

Estimated time: 2 hours

• Integrating vector stores (ChromaDB, Pinecone) with LangChain
• Indexing and querying document embeddings
• Enabling conversational memory and context summarization
• Building plugin-driven tool chains for extended functionality

Module 4: Web App – Chat With PDF

Estimated time: 1.5 hours

• Developing a Chat-with-PDF web application
• Implementing secure file upload and authentication
• Streaming responses and managing large documents
• Backend optimizations for performance and memory

Module 5: Plugins & Tools

Estimated time: 1.25 hours

• Developing custom OpenAI plugins for database access
• Enabling code execution within ChatGPT workflows
• Integrating calculation and data lookup tools
• Connecting custom tools to LangChain agents

Module 6: Distributed Systems & Observability

Estimated time: 0.75 hours

• Setting up Celery and Redis for asynchronous processing
• Implementing real-time server-to-browser streaming
• Adding tracing and telemetry for monitoring AI interactions

Prerequisites

• Solid understanding of Python programming
• Experience with backend development and APIs
• Familiarity with command-line and development environments

What You'll Be Able to Do After

• Integrate ChatGPT into scalable, production-ready applications
• Build multi-step text generation workflows with memory and feedback
• Create custom ChatGPT plugins for database and code interaction
• Develop and deploy a fully-featured Chat-with-PDF web app
• Implement observability and streaming in AI backend systems