Retrieval-Augmented Generation (RAG)
A technique that feeds relevant documents to an AI model at query time so it can answer questions using your actual data instead of guessing.
What It Is
RAG is a two-step process: first, retrieve the most relevant documents or data chunks from your own collection, then feed those chunks into an AI model along with the user’s question so it can generate an answer grounded in real information. Without RAG, the model can only rely on what it learned during training, which may be outdated or incomplete. With RAG, you give the model the specific context it needs right when it needs it. It is the most practical way to make AI “know” things about your business without fine-tuning a custom model.
Why It Matters
RAG solves two major problems at once. First, it reduces hallucinations because the model is answering from your actual documents rather than making things up. Second, it lets you keep your data current without retraining the model, since you just update the documents in your collection. For operators building AI-powered tools, RAG is often the right starting point because it is cheaper and faster than fine-tuning, and it works with any general-purpose model.
In Practice
A common RAG setup: store your company’s knowledge base as embeddings in a vector database like Supabase or Pinecone. When a user asks a question, search for the most relevant chunks, paste them into the prompt as context, and let the model respond. This is how most “chat with your docs” tools work under the hood.