If you've poked around LangChain for more than ten minutes, you've hit the word "chain." It's everywhere — and for good reason. Chains are how you turn a raw language model into something that actually does a job.

What a chain is

A chain is a unit of logic that takes …

By default, an LLM has no idea what you said two messages ago. Every call is stateless — the model processes whatever is in the current prompt and nothing else. If you want a chatbot that remembers the conversation, you have to build that yourself. LangChain's memory modules are how you …

Before you can build a retrieval system, you need to understand what embeddings are and why they exist. Skip this and the rest of RAG won't make sense — you'll be configuring things without knowing what they do.

What an embedding is

An embedding is a list of numbers that represents …

LLM applications fail in ways that are hard to see. A chain returns a bad answer and you don't know whether the prompt was wrong, the retriever missed, or the model just hallucinated. LangSmith is how you look inside.

What LangSmith is

LangSmith is Anthropic's observability platform for LangChain — it …

When you build a retrieval-augmented system, most of the tuning work happens before the LLM ever sees a document. How you split text determines what gets retrieved — and bad splits mean bad answers, even with a great model.

The problem with long documents

LLMs have a context window. You can't …