LangChain is the most-used Python framework for building LLM applications. This course covers chains and prompts, memory for stateful conversations, RAG pipelines for document Q&A, ReAct agents with custom tools, and LangGraph for complex multi-step workflows.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on langchain and llm application development you can find — even without producing a single minute of custom video.
This course is built by people who ship production langchain systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The LangChain expression language (LCEL), prompt templates, model wrappers, and output parsers. How chains compose into pipelines.
ConversationBufferMemory, ConversationSummaryMemory, and the context window management strategies that keep multi-turn conversations coherent.
Loading documents, chunking, embedding with OpenAI or Anthropic, storing in Chroma or FAISS, and the retrieval chains that answer questions about your documents.
The ReAct reasoning loop, built-in tools (search, calculator, SQL), building custom tools, and the agent executor that manages the observation-thought-action cycle.
LangGraph state machines for complex workflows: conditional branching, parallel execution, human-in-the-loop checkpoints, and persistent state.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Chains, prompts, and the LCEL expression language. End-to-end LangChain app from scratch.
Document loading, chunking, embedding, vector store setup, and retrieval chain configuration.
ReAct agents, tool integration, and building custom tools for LangChain agents.
Building stateful multi-step AI workflows with LangGraph. Conditional edges, persistence, and human-in-the-loop.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The core framework. Reading the LCEL source clarifies how chains compose and why the runnable interface enables parallel execution.
Stateful multi-agent workflows built on LangChain. Day 5’s framework. The state graph source explains how conditional edges and checkpoints work.
The vector database used in Day 3 RAG pipelines. The embedding function interface shows how to swap in different embedding models.
The direct SDK that LangChain wraps. Understanding the underlying SDK helps debug LangChain’s behavior when the abstraction leaks.
LangChain is the most deployed Python LLM framework. This course teaches it from the inside out so you know when it’s helping and when to bypass it.
You run notebooks. This course teaches the production LangChain patterns that turn notebook experiments into deployed applications.
This course gives you enough LangChain depth to make an informed decision about when the framework earns its abstraction cost.
The 2-day in-person Precision AI Academy bootcamp covers LangChain and LLM application development hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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