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RAG stands for retrieval augmented generation. Strip the jargon and it is one idea: the model does not know your documents, so before you ask it a question, you look up the relevant passage yourself and paste it into the prompt. Retrieve, augment, generate.
That is the whole trick. Everything else in a RAG pipeline (embeddings, chunking, vector databases, rerankers) exists to make the "look up the relevant passage" step work on thousands of pages instead of one.
This tutorial builds the pipeline from scratch in Python. No LangChain, no vector database, one file. By the end you will know what those tools are doing when you do reach for them. Why the model needs help in the first place
A language model answers from what it saw during training. Ask it about your company's refund policy, last week's meeting notes, or a PDF you were sent this morning, and it has two options: admit it does not know, or produce something confident and wrong. Most models pick the second.
You could paste the whole document into every prompt. That works until the document is longer than the context window, or you have five hundred documents, or you are paying per token and the document is 40 pages. RAG is the fix: send only the few paragraphs that matter for this question. The pipeline in one picture Steps 1 to 3 happen once, when you load the documents. Steps 4 to 6 happen on every question. Step 1: chunk the documents
A passage has to be small enough that a handful of them fit in the prompt, and large enough to still make sense on its own. A few hundred words is the usual compromise.
The overlap matters. Without it, a sentence that straddles a boundary is cut in half and neither chunk contains the full thought.
An embedding is a list of a few hundred or a few thousand numbers. Passages that mean similar things get lists that point in similar directions. That is what lets you search by meaning rather than by exact words.
For a tutorial, the index is a Python list. A vector database is this list plus fast search over millions of rows. You do not need one until the list gets slow.
Embed the question with the same model, then find the chunks whose vectors point the same way. Cosine similarity is the standard measure. Steps 5 and 6: augment and generate Put the retrieved passages in the prompt, tell the model to answer from them, and ask.
RAG stands for retrieval augmented generation.