LLMs have a knowledge problem. They know what was in their training data but nothing about your brand, products, or recent information. RAG bridges this gap by connecting AI to your actual data.

How RAG Works

When you make a request, RAG first searches your knowledge base for relevant information. It might find brand guidelines, product specs, existing content, or company data. This retrieved context is then provided to the LLM alongside your request, grounding its output in your actual information.

Why RAG Matters for Marketing

Without RAG, AI-generated marketing content is generic at best, wrong at worst. RAG enables AI to reference your actual messaging, use correct product details, follow real brand guidelines, and build on existing content. The difference between generic and on-brand is often RAG.

RAG vs Fine-Tuning

Fine-tuning trains a model on your data, baking knowledge in permanently. RAG retrieves information dynamically at runtime. RAG is more flexible since updating your knowledge base immediately changes outputs. Fine-tuning is better for style and behavior. Many systems use both.