A practical guide series for learning LangChain - prompts, models, retrieval, tools, and exactly where LangChain's job ends and LangGraph's begins. Written by people who build with it for a living.
LangChain's core pattern: pipe simple pieces together with | to build a chain.
New to LangChain, or coming from the LangGraph guides and wondering how the two fit together? Start here.
The toolbox versus the wiring diagram - what LangChain actually gives you, and exactly where it hands off to LangGraph.
→The three pieces you'll chain together in almost every LangChain project, and how LCEL's pipe syntax connects them.
Where LangChain earns its keep - connecting a model to documents it wasn't trained on.
APA Mastery runs live, practical sessions on working with modern AI tools - not just theory.
See What's On →The two areas where LangChain's own guidance has shifted the most recently - worth getting the current picture, not an outdated one.
The mechanics underneath everything above, plus how to actually know if a change made your chain better.
What the | operator actually does, and why LCEL chains get streaming and async for free.
The step before retrieval: getting documents in, and splitting them into chunks that actually retrieve well.
→Getting a model to reliably return JSON matching a schema, and when that beats reaching for a full agent.
→How to tell whether a prompt or chunking change actually made your chain better, instead of guessing.
The core chain pattern, the building blocks table, and what's current vs. deprecated, as one printable PDF. Drop your email and it's yours, plus a heads-up whenever new guides go live.
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