LLMs are trained on static data. Ask one about your internal docs, last week's meeting notes, or a product spec — it has no idea. RAG solves this by fetching relevant content at query time and injecting it into the prompt. The model still does the reasoning; you just give it …

Before any LangChain pipeline does anything useful, it needs data. Document loaders are how that data gets in — they fetch content from a source, parse it, and return a list of Document objects with .page_content and .metadata. The rest of the pipeline (splitting, embedding, retrieval) doesn't care where the content …

Three ways to build with LLMs. Each makes different tradeoffs, and picking the wrong one early costs you time when the project grows. This isn't a ranking — it's a map of when each tool actually fits.

Raw API calls

Direct calls to OpenAI, Anthropic, Gemini, or any provider. No framework …

LLMs have no memory between calls. Every time you hit the API, the model starts fresh. To build a chatbot that remembers what was said three messages ago, you have to manually pass that history back in on every request. The problem: each message adds tokens, and token budgets are …

You don't always need a fancy dataset. Sometimes the data you need is already live on the web — you just have to go get it and make sense of it. That's exactly what this pipeline does: scrape a URL, chunk the content, and summarize it using an LLM, all wired …

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 …