The problem RAG solves
Language models know a lot, but they don't know your business. Ask a generic model about your return policy and it will either make something up or refuse to answer. RAG fixes this by retrieving the relevant policy document and including it in the prompt — so the model answers based on facts, not guesses.
How RAG works
Index: Your documents (FAQs, policies, product specs, help articles) are split into chunks and stored in a vector database alongside their semantic embeddings — numerical representations of meaning.
Retrieve: When a question arrives, the system finds the most semantically similar document chunks. "What's your refund policy?" matches the refund section of your terms, even if the exact words differ.
Generate: The retrieved chunks are included in the prompt alongside the question. The model generates an answer grounded in your actual content, with references back to the source documents.
RAG vs fine-tuning
RAG is generally preferred over fine-tuning for business knowledge because it's easier to update (just re-index documents), cheaper to maintain, and produces more verifiable answers. Fine-tuning changes how the model behaves; RAG changes what information it has access to.
We use RAG to power accurate chatbots and internal knowledge assistants for our clients.