Some programming experience7 lessons~10 h

LangChain: Building Real Apps on Top of LLMs

We don't start with "AI is amazing." We start by opening the box: a model API is a function that takes text and returns text. Nothing more. From there you add prompt management, structured output, conversation memory, retrieval over your own documents (RAG), and tool calling — until you have the skeleton of a production AI application. For every LangChain abstraction you'll first see the plain-Python version it replaces, so you always know what's underneath. Finish the first 7 lessons of the Python track before starting this one.

Module 01

Module 1 · What an LLM App Actually Is

What a model API really is, tokens and cost, and your first LCEL chain.

  1. What an LLM App Actually DoesThe model is stateless, the three message roles, how tokens are billed, and what LangChain is really for.
  2. Your First Chain: Wiring Components with |ChatOpenAI, invoke/stream/batch, and how to pick a temperature.
Module 02

Module 2 · Prompts and Structured Output

Manage prompts like code, and turn the model’s prose into data your program can use.

  1. Prompt Templates and Few-Shot ExamplesChatPromptTemplate, five rules for a good system prompt, and the curly-brace trap.
  2. Structured Output: Turning Text into DataPydanticOutputParser, with_structured_output, and what to do when parsing fails.
Module 03

Module 3 · Memory, Private Knowledge, and Tools

Multi-turn conversation, retrieval-augmented generation, and agents with guardrails.

  1. Conversation Memory: There Is No Magic HereMessagesPlaceholder, isolating users by session_id, and what to do when context overflows.
  2. RAG: Teaching the Model About Your CompanyLoad, split, embed, retrieve — and the order to debug in when answers are bad.
  3. Agents and Tool Calling: Letting the Model ActThe @tool decorator, the real five-step loop, and three safety lines you never cross.
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