What Is RAG? Retrieval-Augmented Generation
If you are asking what is RAG, the short version is that it is the most practical way to make a language model answer from your data instead of only its training. This guide is for builders who want a chatbot or assistant that can quote the company handbook, the product docs, or last quarter’s notes. We will walk the whole retrieve-then-generate flow, compare it with fine-tuning, and sketch a minimal build. In this guide The problem RAG solves RAG in one diagram (in words) Step 1: chunk and embed your documents Step 2: retrieve the relevant bits Step 3: generate with context RAG vs fine-tuning Where RAG goes wrong A minimal build plan FAQ Quick answer: RAG (retrieval-augmented generation) means fetching relevant snippets from your own documents and pasting them into the prompt, so the model answers from real sources instead of memory. It is the go-to pattern for accurate, up-to-date, cite-your-sources AI features. The problem RAG solves So what is RAG actually...