Tutorial Shows How RAG Lets Language Models Answer from Your Own Documents

Language models answer from general training knowledge, so they can give confident but wrong responses about a specific business's policies. Retrieval-augmented generation (RAG) fixes this by finding the relevant passages in your documents and placing them in the prompt before the model answers. The guide explains the approach using a library analogy and outlines three steps: retrieve, augment, and generate. It then walks through building a small question-answering system over your own documents using Python and SQLite's full-text search, which requires no extra services and can be tested offline. The author also notes that for small document sets, pasting everything into each prompt may be simpler than building retrieval.