RAG Explained Simply: How to Teach AI About Your Private Data

You've probably seen the term RAG everywhere lately — "RAG pipeline," "RAG chatbot," "build your own RAG app." It sounds complicated, but the idea behind it is actually pretty simple. In this article, I'll explain RAG in plain language, then walk through how it works using a real project I built: Guidely , an internal knowledge assistant that answers questions using a company's own documents. The Problem RAG Solves Large language models (like GPT or Claude) are trained on a huge amount of general knowledge, but they don't know about your specific data — your company's internal docs, your product manuals, your onboarding guides. They also can't be retrained every time a document changes; that's slow and expensive. RAG solves this without retraining the model at all. Basically: RAG means: before answering a question, first go find the relevant pieces of your own documents, and hand those to the AI along with the question. That's it. "Retrieval" (finding the right information) + "Augmented Generation" (the AI answers using that information). Instead of the AI answering from memory alone, it answers using facts you hand it in the moment. The Three Core Pieces Let's break down the three things you need to make this work: chunking , embeddings , and vector search . 1. Chunking — Breaking Documents Into Pieces You can't hand an AI model an entire 200-page document and ask it to search through it efficiently. So the first step is splitting documents into smaller, manageable pieces called chunks . In Guidely, I used a token-window chunker — it splits text based on a fixed number of tokens (roughly, pieces of words) per chunk, rather than just splitting by paragraph or sentence. This matters because: Chunks that are too big waste space and slow things down. Chunks that are too small lose context and produce confusing answers. A token-window approach gives you consistent, predictable chunk sizes, which makes the next steps more reliable. 2. Embeddings — Turning Text Into Numbe