What Is RAG? Retrieval-Augmented Generation Explained for Builders

Retrieval-Augmented Generation — RAG — is the technique behind every "chat with your documents" product. This guide explains RAG in plain terms for builders.

The problem RAG solves: LLMs like Claude and GPT-4 only know what they were trained on. They don't know your company's internal docs or anything past their training cutoff. RAG fixes this by connecting the LLM to your own data at query time.

How it works in three steps: (1) Chunk your documents and convert them to vector embeddings stored in Supabase pgvector or Pinecone. (2) When a user asks a question, convert it to an embedding. (3) Find the most similar document chunks and pass them to the LLM along with the question.

The easiest way to get started: n8n AI agent nodes + Supabase (pgvector). You can have a working prototype in an afternoon. For Python apps: LangChain + Supabase or Pinecone.

RAG works best for: knowledge base Q&A, customer support bots, internal documentation search. It is less suited for: complex multi-step reasoning, creative tasks, or cases where the answer is not explicitly in the documents.