Getting Started with RAG

RAG, retrieval-augmented generation, is one of the most practically useful AI patterns I’ve encountered.

The idea is simple: instead of relying on what a language model memorised during training, you retrieve relevant information at query time and feed it into the model’s context. The model then generates a response grounded in that retrieved information.

Why It Matters for Product Teams

The best AI products I’ve seen don’t ask “what can the model do?” They ask “what does the user need, and how do we get the model the information it needs to help?”

RAG is often the answer.

A Simple Example

Imagine a customer support bot for a SaaS product. Without RAG, it hallucinates policy details. With RAG, it retrieves the actual documentation before answering.

The difference isn’t the model. It’s the architecture.

More on this soon.