Building with LangChain

Building production AI applications requires more than basic API calls—it demands structured patterns for reliability, memory, and knowledge integration. This lab solves the gap between "I can chat with an LLM" and "I can build maintainable AI systems" by teaching reusable components like prompt templates, conversation memory, and retrieval-augmented generation. Whether you're building internal tools, customer-facing assistants, or document intelligence systems, these patterns give you the foundation to ship AI features with confidence.

By the end of this lab, you will be able to:

• Make structured LLM calls using prompt templates and extract typed, validated data with Pydantic schemas
• Build multi-step chains that compose LLM calls with custom logic for complex workflows
• Implement conversation memory to create stateful chatbots that remember context across interactions
• Create RAG systems that ground LLM responses in your own documents using embeddings and vector stores
• Design agents with tools that autonomously decide when and how to retrieve information or take actions

This lab will make use of:

• Python
• Jupyter Notebooks
• LangChain 1.0 and associated libraries
• A Windows-based VM