What exactly is retrieval augmented generation?

What exactly is retrieval augmented generation? Let's try to understand in a simple way. An LLM only knows what's baked into its weights from training. Ask about your private docs and it just guesses. Watch it give a confident wrong answer. RAG fixes this. The idea is simple. Before the model answers, go fetch the right facts from your own files. Here's a stack of your documents. First, we chunk. Each long document gets sliced into small passages, maybe a paragraph each. Watch one page break into tidy little blocks of text. Next, each chunk runs through an embedding model. That turns the text into a vector, a list of numbers that captures its meaning. See every block become an arrow. We drop all those vectors into a vector database. Similar meanings land close together. Watch the dots settle into clusters. This is your searchable index. Now a question comes in. We embed it, too, then search the store for the nearest vectors. The closest chunks light up. Those passages most likely hold the answer. Here's the key move. We paste those retrieved chunks straight into the prompt, right above the question. The model now reads your facts and the question together. Finally, the LLM generates. But now it answers from the passages we handed it, not from memory. Same model, grounded reply, and it can cite the source. So, that's RAG. Chunk your docs, embed them, store the vectors, retrieve the relevant ones, stuff them in the prompt, then generate. The model answers from your data. If RAG finally clicked, tap subscribe. More short explainers are on the way.